Plastics, with their versatility and cost-effectiveness, have become indispensable materials across various industries. However, the improper disposal and mismanagement of plastic waste have led to significant environmental issues, including pollution, habitat destruction, and threats to wildlife. To address these challenges, numerous methods for plastic waste sorting and recycling have been developed. While conventional techniques like near-infrared spectroscopy (NIRS) have been effective to some extent, they face difficulties in accurately classifying chemically similar samples, such as polyethylene terephthalate (PET) and PET-glycol (PET-G), which have similar chemical compositions but distinct physical characteristics. This paper introduces an approach that adapts image sensors and deep learning object detection algorithms; specifically, the You Only Look Once (YOLO) model, to enhance plastic waste classification based on the shape of the waste. Unlike conventional methods that rely solely on spectral analysis, our methodology aims to significantly improve the accuracy and efficiency of classifying plastics, especially when dealing with materials having similar chemical compositions but differing physical attributes. The system developed using image sensors and the YOLO model proves to be not only effective but also scalable and adaptable for various industrial and environmental applications. In our experiments, the results are strikingly effective. We achieved a classification accuracy rate exceeding 91.7% mean Average Precision (mAP) in distinguishing between PET and PET-G, surpassing conventional techniques by a considerable margin. The implications of this research extend far and wide. By enhancing the accuracy of plastic waste sorting and reducing misclassification rates, we can significantly boost recycling efficiency. The proposed approach contributes to a more sustainable and efficient plastic waste management system, alleviating the strain on landfills and mitigating the environmental impact of plastic waste, contributing to a cleaner and more sustainable environment.

These days, many assembly lines are becoming automated, leading to a trend of decreasing defect rates. However, in assembly lines that have opted for partial automation due to high cost of construction, defects still occur. The cause of defects are that the location of the work instructions and the work field are different, which is inefficient and some workers who are familiar with the process tend not to follow the work instructions. As a solution to establishing a system for object detection without disrupting the existing assembly lines, we decided to use wearable devices. As a result, it is possible to solve the problem of spatial constraints and save costs. We adopted the YOLO algorithm for object detection, an image recognition model that stands for “You Only Look Once”. Unlike R-CNN or Fast R-CNN, YOLO predicts images with a single network, making it up to 1000 times faster. The detection point was determined based on whether the pin was fastened after the worker’s hand appeared and disappeared. For the test, 1000 field data were used and the object-detection performance, mAP, was 35%. The trained model was analyzed using seven regression algorithms, among which Xgboost was the most excellent, with a result of 0.15. Distributing labeling and class-specific data equally is expected to enable the implementation of a better model. Based on this approach, the algorithm is considered to be an efficient algorithm that can be used in work fields.