Fatigued driving is one of the leading causes of traffic accidents, and detecting fatigued driving effectively is critical to improving driving safety. Given the variety and individual variability of the driving surroundings, the drivers’ states of weariness, and the uncertainty of the key characteristic factors, in this paper, we propose a deep-learning-based study of the MAX-MIN driver fatigue detection algorithm. First, the ShuffleNet V2K16 neural network is used for driver face recognition, which eliminates the influence of poor environmental adaptability in fatigue detection; second, ShuffleNet V2K16 is combined with Dlib to obtain the coordinates of driver face feature points; and finally, the values of EAR and MAR are obtained by comparing the first 100 frames of images to EAR-MAX and MAR-MIN. Our proposed method achieves 98.8% precision, 90.2% recall, and 94.3% F-Score in the actual driving scenario application.

Sorting unreelable inferior cocoons during the reeling process is essential for obtaining high-quality silk products. At present, silk reeling enterprises mainly rely on manual sorting, which is inefficient and labor-intensive. Automated sorting based on machine vision and sorting robots is a promising alternative. However, the accuracy and computational complexity of object detection are challenges for the practical application of automatic sorting, especially for small stains of inferior cocoons in images of densely distributed cocoons. To deal with this problem, an efficient fine-grained object detection network based on attention mechanism and multi-scale feature fusion, called AMMF-Net, is proposed for inferior silkworm cocoon recognition. In this model, fine-grained object features are key considerations to improve the detection accuracy. To efficiently extract fine-grained features of silkworm cocoon images, we designed an efficient hybrid feature extraction network (HFE-Net) that combines depth-wise separable convolution and Transformer as the backbone. It captures local and global information to extract fine-grained features of inferior silkworm cocoon images, improving the representation ability of the network. An efficient multi-scale feature fusion module (EMFF) is proposed as the neck of the object detection structure. It improves the typical down-sampling method of multi-scale feature fusion to avoid the loss of key information and achieve better performance. Our method is trained and evaluated on a dataset collected from multiple inferior cocoons. Extensive experiments validated the effectiveness and generalization performance of the HFE-Net network and the EMFF module, and the proposed AMMF-Net achieved the best detection results compared to other popular deep neural networks.

Cocoons have a direct impact on the quality of raw silk. Mulberry cocoons must be screened before silk reeling can begin in order to improve the quality of raw silk. For the silk product sector, the cocoons' level of categorization and sorting is crucial. Nonetheless, the majority of mulberry cocoon production facilities in use today choose the cocoons by hand. The accuracy and efficiency of mulberry cocoon plucking can be significantly improved by automatic methods. To increase efficiency, mulberry cocoons must be sorted automatically and intelligently using machine vision. We proposed an effective detection technique based on vision and terahertz spectrum characteristics data for distinguishing defective cocoons, including common and thin shelled defective cocoons. Each mulberry cocoon with a defect had its spatial coordinate and deflection angle computed so that grippers could grasp it. With 3762 photos in our dataset, our approach could detect mAP values up to 99.25% of the time. Furthermore, the GFLOPS of our suggested model was only 8.9 G, and its Parameters were only 5.3 M, making it appropriate for use in real-world application scenarios.