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Quality assessment of fruits plays a key part in the global economy’s agricultural sector. In recent years, it has been shown that fruits are affected by different diseases, which can lead to widespread economic failure in the agricultural industry. Traditional manual visual grading of fruits could be more accurate, making it difficult for agribusinesses to assess quality efficiently. Automatic grading of fruits using computer vision has become a prominent area of study for many researchers. In this study, a deep learning-based model called FruitVision is proposed for the automatic grading of various fruits. The results showed that FruitVision performed all the existing models and obtained an accuracy of 99.42, 99.50, 99.24, 99.12, 99.38, 99.38, 99.17, 98.86, and 97.96% for the apple, banana, guava, lime, orange, pomegranate, Ajwa date, Mabroom date, and mango, respectively, using 5-fold cross-validation. This is a remarkable achievement in the field of AI-based fruit grading systems.

Advanced Driver Assistant System(ADAS) is a critical component of self-driving cars, and lane detection is one of its applications. Convolution Neural Network (CNN) has exhibited its potential to devise state-of-the-art solutions for various computer vision problems and lane detection. However, different scales of lane lines, partial visibility of lane, and the varying resolution of hierarchical features challenge the CNN-based detection methods to localize the lane accurately. In this work, we propose a deep supervision-based model to detect the partially visible lane lines. Hierarchical (Deep) supervision helps the proposed model to trace and classify the lane features at different scales. The simulation of the proposed model gives robust results with partial visibility of lane paint compared to an existing and proven model. The proposed model's accuracy is as high as 92% for partially visible lane and 99% for full visible lane, which stands to the right in its peer work comparison. [ABSTRACT FROM AUTHOR]

• The research is conducted using a self-collected and labeled surface crack dataset consisting of 3000 images. • The weights obtained from Mask RCNN model pre-trained on COCO dataset in phase I are utilized for fine-tuning in phase II. • Additionally, the hyperparameters are optimized resulting in reduced training time and improved accuracy. • The technique of freeing and unfreezing specific layers of the proposed architecture has improved the model performance. The detection of surface cracks or defects in roads, sewage pipes, pavements, shield tunnels lining, etc. has become a promising area of research creating a significant impact on various application domains. Maintenance and monitoring of the manufactured and natural structures are crucial for their correct functioning and integrity. An effective way to predict structural degradation and deterioration is by detecting crack formation, which defines bad health for any artificial or natural structure. Deep learning-based techniques are prominent in accurately identifying the cracks as opposed to the conventional machine learning techniques, which require large processing time and involves handcrafted features. A deep learning-based architecture for automatically detecting and segmenting the cracks in concrete based material surface is presented in the paper. The training and testing results are demonstrated on a newly created dataset consisting of 3000 images of surface cracks. We have manually annotated the cracks present in each image by drawing a bounding box and segmented mask around them. The testing results are evaluated on parameters such as crack detection accuracy, mean average precision rate (mAP), and crack detection speed. To overcome the data collection and labeling barriers, a part of the dataset is made publicly available for research purposes. The training speed and the convergence rate of the proposed algorithm are optimized by using the concept of transfer learning through freezing and unfreezing certain layers of the network. Additionally, the choice of appropriate hyperparameters for improving the model accuracy is discussed in the article. The performance of the model is evaluated against the ground truth labels generated. The AP is reported 74.156% and 93.445% at IoU (Intersection over union) threshold of 0.7 and 0.5 respectively for crack segmentation. [ABSTRACT FROM AUTHOR]