AADH-YOLOv5: improved YOLOv5 based on adaptive activate decoupled head for garbage detection
Yun Jia, Jie Zhao, Lidan Yu · Journal of Electronic Imaging · 2023
As the issue of garbage pollution becomes increasingly urgent, accurate and effective garbage detection can play a crucial role in minimizing environmental pollution and facilitating the conversion of waste into energy. However, the accuracy of current waste detection algorithms is limited. We propose an approach called the adaptive activate decoupled head for YOLOv5 (AADH-YOLOv5) for detecting garbage. Our model improves localization accuracy without significantly increasing floating point of operations (FLOPs) or parameters. Extensive testing on the newly created domestic garbage dataset-6 demonstrates that AADH-YOLOv5 outperforms YOLOv5l and Cascade R-CNN, achieving a 5.4% and 4.2% improvement in [email protected], respectively. Our study also contributes to the field by providing a new dataset for garbage detection research.