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Thanks to the precision agriculture technologies developed in recent years, many processes such as irrigation, fertilization, spraying, weeding and harvesting of agricultural products can be done by autonomous systems. Especially in some plant species such as sunflower, when to apply these processes is largely decided according to the developmental stage of the plant. In this study, deep learning methods were used to classify the developmental stages of sunflower plants. Since the images taken with the drone are high resolution, each of them is divided into 6 equal parts, and then 8 classes are determined and the images belonging to each class are extracted. A data set consisting of 12800 images in total, 1600 in each class, was created. Six different deep learning models, namely AlexNet, InceptionV3, ResNet101, DenseNet121, MobileNet and Xception, were tested with Sgd, Adam and Rmsprop optimization methods and their performances were compared. In order to evaluate the success of the models correctly, the trained models were also tested on a second data set created with images taken from a different terrain and high success rates were obtained. In addition, a 7-class test set was created for images that could not be clearly determined at which stage the plant was in, and the success rates of the models were tested. It was observed that the success rate was very low for the images in the 7-8 intermediate class, and the filters used in the image processing techniques that would increase the success rate for this class were applied to the images, and the models were retrained and the results were evaluated. [ABSTRACT FROM AUTHOR]

Fire is a natural disaster that causes ecological, social and economic damage. With global warming and the widespread use of explosive/flammable chemicals, fires have become one of the most important problems for humanity. Early fire detection is critical to minimize destruction. For this reason, studies on fire detection through images have begun to be carried out in order to detect fires early. In recent studies for fire detection from images deep learning algorithms are generally used. These studies focus on the analysis of images taken from cameras with models running on powerful servers. With the developments in mobile devices and internet of things, images can analyzed on edge devices. In the study fire and smoke detection model which requires low processing power has been developed to enable the images to be analyzed on mobile device without transferring them to server. The MobileNet convolutional neural network was revised, the last 3 layers were removed and replaced with a flattening layer and a dense layer consisting of two nodes. Fire and smoke detection model, the method with the highest accuracy was determined among the models developed using revised MobileNet, original MobileNet, MobileNetV2, EfficientNetB0, ShuffleNet, NASNetMobile and PeleeNet convolutional neural networks. To train and test the models, 80% of the dataset consisting of 43,355 images was used for training and 20% for testing. According to test results, highest accuracy rate of 98.37% was achieved in the revised MobileNet. [ABSTRACT FROM AUTHOR]

Driver drowsiness detection is an important issue to prevent traffic accidents. 40% of severe traffic accidents are due to drowsiness. Various methods are used for driver drowsiness detection. One of the driver drowsiness detection method is driver drowsiness detection based on the analysis of signals such as EEG and ECG. Another driver drowsiness detection method is driver drowsiness detection based on vehicle-driver interaction. The last driver drowsiness detection method used in the study is driver drowsiness detection from images. This method is more advantageous than the other two methods in terms of cost and usability because no driver intervention required. Classical image processing techniques and deep learning algorithms are used for driver drowsiness detection from images. Recent driver drowsiness detection studies are based on deep learning models. In addition, the model to be developed will need to be able to work on mobile devices in order to ensure widespread use. In the study, Convolutional Neural Networks were used for driver drowsiness detection on mobile devices. In order to increase the success rate of the model, the pre-trained model was reused with the transfer learning technique. The model developed for the training consists of 14 layers and 1,236,217 parameters. The dataset consists of two categories, open-eye and closed-eye images. The developed model achieved 95.65% accuracy, 95.86% precision, 94.32% recall and 95.17% f1 score which achieved better results than previous studies. A dataset of 2425 images was used to train the model. [ABSTRACT FROM AUTHOR]

Shoulder implants must be replaced after a certain period of time. But determining the manufacturer or model of the implant during this change is often a mistake-prone and difficult process for medical experts. The aim of this study is to identify 4 different implant manufacturers from 597 shoulder implant X-ray images. For this purpose, both pre-trained CNN architectures (DenseNet201, DenseNet169, InceptionV3, NasNetLarge, VGG16, VGG19 and Resnet50) and cascade models that feed these architectures with YOLOv3 detection algorithm were created and the classification performances of these models were compared. The task of the YOLOv3 detection algorithm in cascade models is to detect the head area of the shoulder implants and give this area as an input to CNN architectures. In addition, traditional machine learning methods were combined with the ensemble learning method and their performance on the data set was revealed. The highest classification performance was achieved in the cascade DenseNet201 model with an accuracy rate of 84.76%. This rate is higher in the literature than in a different study using similar dataset. Classification accuracy of ensemble models is substantially lower than CNN models. Also, the classification accuracy of YOLO supported cascade models is higher than individual CNN models. That is, focusing on the head of the implant with the YOLOV3 detection algorithm has increased the classification accuracy. This method will inspire future studies in this field. [ABSTRACT FROM AUTHOR]

Bees are one of the oldest living species in the world, having a major impact on the development of living species. The continuity of plants at the bottom of the food chain is directly related to the pollination of bees. Bees are a global insurance because of this characteristic. For this reason, it is very important to check the health status of bees. Depending on the technology developed nowadays, it is possible to control the health status of bees remotely with real-time image processing applications. In the study, feature extraction methods, which are the strengths of deep learning, were executed from two different arms and aggressive changes in images were detected. In the classification process; Instead of Softmax classifier based on probability calculation, multi-layer feedback artificial neural network (MLFB-ANN) model has been used. The success of the designed system has also been compared with the Softmax classifier. As a result of experimental studies, 93.07% success rate can be achieved with Softmax classifier for six different bee diseases on the same data set, while 95.04% success rate has been obtained with the developed system. In this study, a hybrid method based on deep learning methods was proposed for the classification of bee diseases and successful results were obtained. [ABSTRACT FROM AUTHOR]

Image processing applications in real-time systems have become a popular topic in recent years. Deep learning methods, one of the sub-branches of artificial intelligence, and image processing algorithms used in the field of object detection from images can be used together. In this way, applications are developed in many areas such as autonomous cars, autonomous unmanned aerial vehicles, assist robot technologies, assistant technologies for disabled and elderly individuals. This study aims to detect the tactile paving surfaces with deep learning methods in order to design an assistive technology system that can be used by visually impaired individuals, autonomous vehicles and robots. Contrary to traditional image processing algorithms, deep learning methods and image processing algorithms are used together in this study. The YOLO-V3 model, which is one of the best methods of object detection, is combined with the DenseNet model to create the YOLOV3-Dense model. YOLO-V2, YOLO-V3 and YOLOV3Dense models were trained on the Marmara Tactile Paving Surface (MDPY) dataset, which was created by the researchers and included 4580 images and their performances were compared with each other on the test dataset. It was observed that YOLOV3-Dense model is better than other models in detecting tactile paving surface with 89% F1-score, 92% mean average Precision(mAP) and 81% IoU values. [ABSTRACT FROM AUTHOR]