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Integrate the traditional Theory of Planned Behavior (TPB) and Technology Acceptance Model (TAM) to build an empirical research model on the relationship between perceived behavioral control, intelligent facilities, facility convenience, incentive measures, and willingness and behavior of garbage classification. Conduct a questionnaire survey on the willingness and behavior of garbage classification among college students in Nanchang City, and use structural equation modeling to explore the key factors influencing college students' garbage classification.Combine ideological and political education with course content, and implement teaching reform in embedded artificial intelligence professional courses. Guide students to practice using lightweight visual model deployment technology to improve garbage classification assistance and human-computer interaction performance in homework and other links, thus achieving the dual goal of teaching and ideological and political education. The research results show that perceived behavioral control, facility convenience, and incentive measures have a significant positive impact on willingness of garbage classification, and perceived behavioral control is the main key factor. And the positive impact of intelligent facilities on garbage classification, and ensure the sustainable development of the ecological environment by strengthening situational motivation, building interactive robot lectures, establishing green carbon accounts to offset practical credits and incorporating the evaluation system for excellence and advancement, as well as professional course content development. [ABSTRACT FROM AUTHOR]

The garbage instance segmentation in complex stacked scenes is affected by serious occlusion and high density, which makes it more difficult to detect. To solve this problem, a case segmentation method combining DCT-Mask and the idea of double-layer feature fusion network were proposed, which is used for garbage case segmentation in highly stacked scenes. At the network structure level, firstly, the feature data was decoupled in the data pre-processing link, and the impact of stack on occluded object features was reduced through dual branch feature fusion, so as to solve the case segmentation problem under complex stack occlusion. To solve the problem of dense confusion in this scenario, a cascade classifier was incorporated into the candidate box classification regression part, and the loss function of dividing network branches was optimized. The experimental results show that the AP50, the average accuracy mAP and other indicators of this method have been greatly improved, and have a good segmentation effect and a certain degree of interpretability. [ABSTRACT FROM AUTHOR]

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view of the large amount of garbage and the fact that a picture contains multiple garbage objects, this study proposes a garbage detection and classification algorithm based on the improved MobileNet network, which integrates the MobileNet network into YOLOv5(You Only Look Oncev5) target detection algorithm. At the same time, the CBAM(Convolutional Block Attention Modul) module is introduced in the backbone to filter meaningful information, and the vision transformer is used to aggregate and form image features. In addition, the weighted bidirectional feature pyramid network is used to distinguish the contribution of different features. At the same time, the ECA(Efficient Channel Attention) module is introduced to combine the image features and transmit them to the prediction layer. Finally, in order to obtain better performance when there is occlusion between garbage targets, soft-NMS(soft-Non Maximum Suppression) method and Alpha-IoU(Alpha-Intersection over Union) loss function is used to predict the extracted features. The experimental results show that the method proposed in this study can realize the location and recognition of multi-target and multi-category garbage., and the mAP(mean Average Percision) value reaches 90.31%, which is 4.95% higher than that of YOLOv5 network, and the processing speed is shortened by about 2.4 seconds. Compared with the Faster R-CNN(Region-based Convolutional Neural Network) algorithm which integrates ResNet(Residual Network) network, the algorithm proposed in this study improves the processing efficiency on the premise of ensuring the accuracy. [ABSTRACT FROM AUTHOR]

With the implementation of garbage classification, perishable waste has become increasingly concentrated. This has led to a significant change in the VOC release characteristics at residential garbage collection points, posing a potential risk with unknown characteristics. This study investigated the release characteristics, odor pollution, and health risks of VOCs at garbage collection points under different classification effectiveness, seasons, garbage drop-off periods, and garbage collection point types. The results showed that the average concentration of VOCs released from the garbage sorting collection points (SPs) was 341.43 ± 261.16 μg/m3, and oxygenated compounds (e.g., ethyl acetate and acetone) were the main VOC components. The VOC concentration increased as the community classification effectiveness improved, and it was higher in summer (followed by spring, autumn, and winter). Moreover, the VOC concentrations were higher in the evenings than in the mornings and at centralized garbage collection points (CPs) than at SPs. Further, odor activity value (OAV) assessments indicated that acrolein, styrene, and ethyl acetate were the critical odorous components, with an average OAV of 0.87 ± 0.85, implying marginal odor pollution in some communities. Health risk assessments further revealed that trichloroethylene, benzene, and chlorotoluene were the critical health risk substances, with an average carcinogenic risk (CR) value of 10−6–10−4, and a non-carcinogenic risk (HI) value < 1. These results indicated that HIs were acceptable, but potential CRs existed in the communities. Therefore, VOC pollution prevention and control measures should be urgently strengthened at the garbage collection points in high pollution risk scenarios. [ABSTRACT FROM AUTHOR]

With the improvement of urbanization and living standards, waste disposal has become an urgent environmental problem. In recent years, object detection networks based on deep learning have greatly improved classification accuracy and speed by learning feature expression and object detection through deep neural networks. However, with the increase of model depth and number of parameters, object detection models based on deep neural networks require a lot of computing and storage resources, and are difficult to deploy on edge devices. To solve this problem, this paper proposes a lightweight garbage classification object detection algorithm based on improved YOLOv7. Firstly, the DW-SPPFCSPC module is constructed according to the depth-separable volume product, replacing the original SPPCSPC module. Second, the ECA channel attention mechanism was added to the backbone of YOLOv7. By paying more attention to important feature channels, the model can better capture important information in images and thus improve the performance of target detection. Finally, the network channel is pruned by analyzing the network dependency graph. Experimental results show that the model size is greatly reduced by this method. Compared with the original YOLOv7 model, the number of parameters is reduced to 6.05% and floating-point operations (FLOPs) are reduced to 1/16 with a loss of only 0.56% in average accuracy. The method is tested on VOC and H2O data sets, and good compression results are obtained.

This paper proposes a framework for AI-driven municipal solid waste classification service design and management, with an emphasis on advancing sustainable urban development. This study uses narrative research and case study methods to delve into the benefits of AI technology in waste classification systems. The framework includes intelligent recognition, management strategies, AI-based waste classification technologies, service reforms, and AI-powered customer involvement and education. Our research indicates that AI technology can improve accuracy, efficiency, and cost-effectiveness in waste classification, contributing to environmental sustainability and public health. However, the effectiveness of AI applications in diverse city contexts requires further verification. The framework holds theoretical and practical significance, offering insights for future service designs of waste management and promoting broader goals of sustainable urban development. [ABSTRACT FROM AUTHOR]

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Garbage classification has always been an important issue in environmental protection, resource recycling, and social livelihood. However, garbage classification takes a lot of time and effort. Moreover, garbage classification directly affects the health of workers. Currently, due to the development of artificial intelligence, advanced garbage classification robots are being used more and more in recycling factories. With the sufficient support of robots integrated with artificial intelligence technology, garbage will be more and more quickly processed and accurately classified. Therefore, this study presents an efficient and simple garbage classification model based on deep learning technology. This model will automatically and accurately classify garbage, thereby freeing up human labors. In this paper, the ResNet-50 model was used to develop the system. The input data includes images of garbage types to perform classification, and 3 different groups of garbage will be classified. The experimental results demonstrate the effectiveness of this model.

Effective waste management and recycling are essential for sustainable development and environmental conservation. It is a global issue around the globe and emerging in Saudi Arabia. The traditional approach to waste sorting relies on manual labor, which is both time-consuming, inefficient, and prone to errors. Nonetheless, the rapid advancement of computer vision techniques has paved the way for automating garbage classification, resulting in enhanced efficiency, feasibility, and management. In this regard, in this study, a comprehensive investigation of garbage classification using a state-of-the-art computer vision algorithm, such as Convolutional Neural Network (CNN), as well as pre-trained models such as DenseNet169, MobileNetV2, and ResNet50V2 has been presented. As an outcome of the study, the CNN model achieved an accuracy of 88.52%, while the pre-trained models DenseNet169, MobileNetV2, and ResNet50V2, achieved 94.40%, 97.60%, and 98.95% accuracies, respectively. That is considerable in contrast to the state-of-the-art studies in the literature. The proposed study is a potential contribution to automating garbage classification and to facilitating an effective waste management system as well as to a more sustainable and greener future. Consequently, it may alleviate the burden on manual labor, reduce human error, and encourage more effective recycling practices, ultimately promoting a greener and more sustainable future. [ABSTRACT FROM AUTHOR]

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China has been implementing garbage classification to improve resource recycling for many years. Since garbage classification is essentially a social activity, it needs the active participation of the public. However, the phenomenon of "high practice, low effect" is widespread in most cities. Therefore, this paper uses the data from Sina Weibo to analyze the reasons for the poor garbage classification effect. First, the key factors affecting residents' willingness to participate in garbage classification are identified based on the text-mining method. Further, this paper analyzes the reasons that promote or hinder the residents' intention of garbage classification. Finally, the resident's attitude towards garbage classification is explored by the score of the text's emotional orientation, and further the reasons for the positive and negative emotional orientation are analyzed, respectively. The main conclusions are as follows: (1) The proportion of residents holding negative sentiment towards garbage classification is as high as 55%. (2) Residents' positive emotions are mainly caused by the public's sense of environmental protection inspired by publicity and education, and the incentive measures taken by the government. (3) The main reasons for negative emotions are imperfect infrastructure and unreasonable garbage sorting arrangements. [ABSTRACT FROM AUTHOR]

Solid waste has surged in rural China, home to more than 540 million people. To preserve the environment, the Chinese government has piloted garbage classification programs. However, little is known about whether and to what extent classifying garbage affects people's subjective well-being—should its effects be positive, people would be more amenable to classifying garbage, making it easier to entrench garbage classification practices and programs and ultimately improve the environment. Accordingly, we analyze the impact of garbage classification on subjective well-being using the 2020 China Land Economic Survey data. An endogenous treatment regression model is utilized to address self-selection into garbage classification programs. We find that this simple and somewhat mundane practice can significantly improve people's happiness and life satisfaction. These results reaffirm the compound benefits of allocating more public resources to accelerate the adoption of garbage classification in rural areas. [ABSTRACT FROM AUTHOR]

• An machine vision system is developed for garbage detection and classification. • A new deep learning method is proposed and embedded into the vision system. • This approach can achieve real-time garbage detection and classification. Waste recycling is a critical issue for environment pollution management while garbage classification determines the recycling efficiency. In order to reduce labor costs and increase garbage classification capacity, a machine vision system is established based on the deep learning and transfer learning. In this new method, an improved MobileNetV2 deep learning model is proposed for garbage detection and classification, where the attention mechanism is introduced into the first and last convolution layers of the MobileNetV2 model to improve the recognition accuracy and the transfer learning uses a set of pre-trained weight parameters to extend the model generalization ability. In addition, the principal component analysis (PCA) is employed to reduce the dimension of the last fully connected layer to enable real-time operation of the developed model on an edge device. The experimental results demonstrate that the proposed method generates 90.7 % of the garbage classification accuracy on the "Huawei Cloud" datasets, the average inference time is 600 ms on the raspberry Pi 4B microprocessor, and the model volume compression is 30.1 % of the basic MobileNetV2 model. Furthermore, a garbage sorting porotype is designed and manufactured to evaluate the performance of the proposed MobileNetV2 model on the real-world garbage identification, which turns out that the average garbage classification accuracy is 89.26 %. Hence, the developed garbage sorting porotype can be used a effective tool for sustainable waste recycling. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Guangxi Normal University - Natural Science Edition is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Most existing methods of garbage classification utilize transfer learning to acquire acceptable performance. They focus on some smaller categories. For example, the number of the dataset is small or the number of categories is few. However, they are hardly implemented on small devices, such as a smart phone or a Raspberry Pi, because of the huge number of parameters. Moreover, those approaches have insufficient generalization capability. Based on the aforementioned reasons, a promising cascade approach is proposed. It has better performance than transfer learning in classifying garbage in a large scale. In addition, it requires less parameters and training time. So it is more suitable to a potential application, such as deployment on a small device. Several commonly used backbones of convolutional neural networks are investigated in this study. Two different tasks, that is, the target domain being the same as the source domain and the former being different from the latter, are conducted besides. Results indicate with ResNet101 as the backbone, our algorithm outperforms other existing approaches. The innovation is that, as far as we know, this study is the first work combining a pre-trained convolutional neural network as a feature extractor with extreme learning machine to classify garbage. Furthermore, the training time and the number of trainable parameters is significantly shorter and less, respectively. [ABSTRACT FROM AUTHOR]

The problem of environmental pollution has increased recently and poses a threat to both humanity and the environment. A big waste problem arises as a result of unplanned urbanization, industrialization, and irresponsible behavior of people around the world. This problem restricts our living standards and causes us to live in an unhealthy environment.. Since wastes are generally known as garbage, they are removed from the environment without trying to be recovered. In this case, the self-dissolution time of the waste in nature takes many years without recycling. By prolonging the life of waste and separating it, environmental pollution is reduced and the world becomes more livable by contributing to recycling. In this study, which was carried out using deep learning models and machine learning methods, the classification process was carried out using the waste separation data set, which makes our environment more livable and helps recycling. In this study, results were obtained with AlexNet, GoogLeNetGoogLeNet, ResNet50, DenseNet201, ShuffleNet, and SqueezeNet architectures in the detection of waste separation with artificial intelligence techniques. In the next step, feature maps obtained from deep architectures are classified in support vector machines, k-nearest neighbor, and decision trees. By comparing the results of the architectures, the best architecture was selected for this problem. As a result of the experiments, the hybrid model we proposed achieved an accuracy value of 85.94%. [ABSTRACT FROM AUTHOR]

In response to the call for implementing national waste classification, this paper proposes an intelligent waste classification system based on the improved MobileNetV3-Large, which can raise the national awareness of waste classification through the combination of software and hardware. The software module is based on WeChat applet and offers functions for image recognition, text recognition, speech recognition, points-based quiz and so on. The hardware module is based on Raspberry Pi and covers image shooting, image recognition, automatic classification with automatic announcement and so on. The algorithm model applied to the image classification adopts a network model based on MobileNetV3-Large. This network model is enabled to classify garbage images through deep separable convolution, inverse residual structure, lightweight attention structure and the hard_ swish activation function. The text classification model adopts a network model based on LSTM, extracts text features through word embedding, enhancing the effect of garbage text classification. After testing, the system can leverage deep learning to realise intelligent garbage classification. The image recognition accuracy of the algorithm model was found to reach 81%, while the text recognition accuracy was as high as 97.61%. [ABSTRACT FROM AUTHOR]

To improve the efficiency of social garbage classification, a garbage classification method based on a small convolutional neural network (CNN) is proposed in this paper. For low accuracy caused by light and shadow interference, an adaptive image-brightening algorithm is developed to average the brightness of the background in the image preprocessing stage, and a threshold replacement method is used to reduce shadow noise. Then, the Canny operator is used to assist in cropping the blank background in the image. For debugging low efficiency caused by the complex network, the neural network is optimized based on the MLH-CNN model to make its results simpler and equally efficient. Experimental results show the preprocessing in this study can improve the accuracy of model garbage classification. The CNN model in this study can achieve an accuracy of 96.77% on the self-built dataset and 93.72% on the TrashNet dataset, which is higher than the 92.6% accuracy of the MLC-CNN model. The network optimizer can also enhance the classification ability of the network model using the Adamax optimization algorithm based on Adam variants. In this paper, the network model derived from training is combined with the host computer software to design a garbage detection page so the model has a wider range of uses, which has a good effect on promoting the development of social environmental protection and improving residents' awareness of environmental protection. [ABSTRACT FROM AUTHOR]

In recent years, People pay more and more attention to garbage classification and recycling, but garbage classification consumes a lot of manpower and material resources and the sorting efficiency is low. To solve the problem that the garbage detection method based on rectangular bounding box is not effective enough when applied to multi-classification environment, a garbage detection method based on improved Mask R-CNN is proposed. Instead of the traditional ResNet, this method uses the improvedResNeXt101asthebackbonenetworkforfeatureextraction, which improves the accuracy of objectdetectionandtheaccuracyofbackgroundboundarysegmentation. Experimental results show that compared with the traditional Mask R-CNN model, the proposed models average classification accuracy is 91. 1 %, improved by 2. 35 %. Finally, the experimental comparison with the current popular object detectionalgorithmsshowsthattheclassificationaccuracyandsegmentationaccuracyoftheproposedalgorithm are excel ent, which proves the feasibility and e fectiveness of the proposed method in the garbage detection task. [ABSTRACT FROM AUTHOR]

In order to solve the classification model's shortcomings, this study suggests a new trash classification model that is generated by altering the structure of the ResNet-50 network. The improvement is divided into two sections. The first section is to change the residual block. To filter the input features, the attention module is inserted into the residual block. Simultaneously, the downsampling process in the residual block is changed to decrease information loss. The second section is multi-scale feature fusion. To optimize feature usage, horizontal and vertical multi-scale feature fusion is integrated to the primary network structure. Because of the filtering and reuse of image features, the enhanced model can achieve higher classification performance than existing models for small data sets with few samples. The experimental results show that the modified model outperforms the original ResNet-50 model on the TrashNet dataset by 7.62% and is more robust. In the meanwhile, our model is more accurate than other advanced methods.

In recent years, garbage classification has become a hot topic in China, and legislation on garbage classification has been proposed. Proper garbage classification and improving the recycling rate of garbage can protect the environment and save resources. In order to effectively achieve garbage classification, a lightweight garbage object detection model based on deep learning techniques was designed and developed in this study, which can locate and classify garbage objects in real-time using embedded devices. Focusing on the problems of low accuracy and poor real-time performances in garbage classification, we proposed a lightweight garbage object detection model, YOLOG (YOLO for garbage detection), which is based on accurate local receptive field dilation and can run on embedded devices at high speed and with high performance. YOLOG improves on YOLOv4 in three key ways, including the design of DCSPResNet with accurate local receptive field expansion based on dilated–deformable convolution, network structure simplification, and the use of new activation functions. We collected the domestic garbage image dataset, then trained and tested the model on it. Finally, in order to compare the performance difference between YOLOG and existing state-of-the-art algorithms, we conducted comparison experiments using a uniform data set training model. The experimental results showed that YOLOG achieved A P 0.5 of 94.58% and computation of 6.05 Gflops, thus outperformed YOLOv3, YOLOv4, YOLOv4-Tiny, and YOLOv5s in terms of comprehensive performance indicators. The network proposed in this paper can detect domestic garbage accurately and rapidly, provide a foundation for future academic research and engineering applications. [ABSTRACT FROM AUTHOR]

【Objective/Meaning】Garbage classification was an important way to improve the rural ecological environment and rural ecological civilization. As the main body of rural environmental governance, the farmers ’ mastery of environmental information played a key role in their decision-making on garbage classification. 【Methods/Procedures】Based on the field survey data of Fujian, Anhui and Shaanxi provinces, the impact of information acquisition on the willingness of farmers to sort garbage was analyzed, and the mediating effect of value perception and the moderating effect of the family economic level of farmers were tested, in order to explore the internal mechanism that affected the willingness of farmers to sort garbage. 【Results/Conclusions】The results showed that: 1) Information acquisition had a significant promoting effect on the willingness of farmers to sort garbage. 2) Analysis of the mediating effect: the value perception also played a significant role in promoting the willingness of farmers to sort garbage, and played a partial mediating role in the impact of information acquisition on the willingness of farmers to sort garbage. 3) Moderating effect and threshold effect: the income level of rural households played a negative regulatory role in the impact of information acquisition on the willingness of farmers to sort garbage. And when the total family income exceeded 46,000 yuan, the information acquisition would no longer play a promoting role. [ABSTRACT FROM AUTHOR]

Management of garbage classification is a general term for a series of activities to sort, store and transport garbage into public resources according to certain regulations or standards. Current garbage classification systems have several drawbacks, such as inability to identify multiple garbage categories, and high dependence on the surrounding environment. To address these issues, this paper has proposed the Real Time Multi-Modal Garbage classification System (abbreviated as RMGCS). It consists of two sub systems: an indoor garbage classification applet (abbreviated as IGCA) and an outdoor garbage classification system (abbreviated as OGCS). IGCA provides users with three methods of garbage classification, and OGCS provides users with outdoor real-time multi-target garbage classification and can dynamically update the recognition model. RMGCS achieves real-time, accurate, and multimodal classification. Finally, the experiments with RMGCS show that our approaches are effective and efficient. [ABSTRACT FROM AUTHOR]

• A robust framework combined saliency detection and image classification is proposed. • The smallest rectangle containing the saliency region is used for segmentation. • Data fusion is used to generate synthetic images for improving the robustness. Using deep learning to solve garbage classification has become a hot topic in computer version. The most widely used garbage dataset Trashnet only has garbage images with a white board as background. Previous studies based on Trashnet focus on using different networks to achieve a higher classification accuracy without considering the complex backgrounds which might encounter in practical applications. To solve this problem, we propose a framework that combines saliency detection and image classification to improve the generalization performance and robustness. A saliency network Salinet is adopted to obtain the garbage target area. Then, a smallest rectangle containing this area is created and used to segment the garbage. A classification network Inception V3 is used to identify the segmented garbage image. Images of the original Trashnet are fused with complex backgrounds of the other saliency detection datasets. The fused and original Trashnet are used together for training to improve the robustness to noises and complex backgrounds. Compared with the image classification networks and classic target detection algorithms, the proposed framework improves the accuracy of 0.50% − 15.79% on the testing sets fused with complex backgrounds. In addition, the proposed framework achieves the best performance with a gain of 4.80% in accuracy on the collected actual dataset. The comparisons prove that our framework is more robust to garbage classification in complex backgrounds. This method can be applied to smart trash cans to achieve automatic garbage classification. [ABSTRACT FROM AUTHOR]

Existing educational toys for teaching garbage classification fail to teach about its benefits and positive results. Thus, children do not fully understand the logic behind garbage classification. We summarized the design strategies of garbage classification educational toys according to parents' evaluations of existing toys and the literature on children's memory characteristics. Presenting children with all the system information related to garbage classification is essential for their logical understanding. Using interactive formats and personified images enhances children's desire to play with toys. Based on the above strategies, we designed an intelligent trash can system toy: Incorrect garbage input displays an uncomfortable expression and sad voice. Correct garbage input triggers happy expressions and positive sounds. An animated story then shows how the garbage is treated and recycled into something new. The results of a contrast experiment showed that the accuracy rate of children's garbage classification was significantly raised after playing with the designed toy for two weeks. The toy also promoted children's garbage-sorting behavior in daily life. When seeing trash misclassified, the children would correct the mistakes and take the initiative to share relevant knowledge about garbage disposal.