A beach litter detection method based on improved YOLOv7

Beach litter monitoring and cleaning is a difficult and involved undertaking since beach litter contamination poses a serious hazard to human health and marine ecosystems. Due to the low monitoring efficiency, limited detection range, and unreliable timeliness of typical human surveys, this work proposes a beach litter detection method based on improved YOLOv7. First, YOLOv7 is initially merged with Quantitative awareness RepVGG (QARepVGG) to achieve rapid computation and reduce the number of model parameters. Second, the SimAM attention mechanism is included to improve the network's capacity for feature extraction from the targeted region of the picture. To further enhance the original path aggregation network (PAN), the Bi-directional feature pyramid network (BiFPN) structure is merged with it. As a result, the network is more effectively able to identify targets of various sizes and recognize the characteristics of different litter sizes. Experimental findings on the self-constructed dataset demonstrate that the modified model has strong identification capability for eight different categories of beach litter. The revised model outperforms YOLOv7 in terms of mean average precision (mAP) improvement by 5.8%, frame per second (FPS) improvement by 17, and the improved model has the highest recognition accuracy for styrofoam, plastic, and paper. When compared to a number of widely used detection models, our improved model has the best performance. These detection results in real scenarios show that the improved model is capable of detecting beach litter in real time.