Research on Recyclable and Hazardous Waste Detection Methods based on YOLOv8 - Tang, Zilin; Yu, Shengjie; Zhang, Zeyuan; Yang, Chunlei; Song, Bin (International Journal of Computer Science and Information Technology, 2025)
In modern environmental governance, garbage classification is crucial. However, traditional manual sorting and machine learning methods based on manual features have limited generalization capabilities in complex environments, especially in the detection tasks of recyclable garbage and hazardous garbage, which are easily affected by illumination changes, morphological similarity and background interference, resulting in low detection accuracy. This paper introduces a recyclable garbage and hazardous garbage detection method based on YOLOv8. By integrating data enhancement, category imbalance processing, multi-scale training and optimized non-maximum suppression (NMS) technology, the robustness and detection accuracy of the model in complex scenarios are improved, and the category imbalance problem is alleviated by optimizing the loss function. The adaptability of the model is enhanced by multi-scale training, realizing an efficient and practical garbage classification detection system. Experimental results show that compared with traditional HOG+SVM, CNN and other methods, the detection system can provide higher detection accuracy and stronger generalization ability while ensuring real-time performance, and has excellent performance in the recognition of hazardous garbage and recyclable garbage categories.