Improved YOLOv7 Algorithm for Floating Waste Detection Based on GFPN and Long-Range Attention Mechanism

Abstract Floating wastes in rivers possess characteristics including small scale, low pixel density and complex backgrounds, which makes it prone to false and missed detection during image analysis, thus resulting in a degradation of detection performance. In order to tackle these challenges, a floating waste detection algorithm based on YOLOv7 is proposed, which combines the improved GFPN and long-range attention mechanism. Firstly, we import the improved GFPN to replace the Neck of YOLOv7, thus providing more effective information transmission that can scale into deeper networks. Secondly, the convolution-based and hardware-friendly long-range attention mechanism is introduced, allowing the algorithm to rapidly generate attention map with a global receptive field. Finally, the algorithm adopts the WiseIoU optimization loss function to achieve adaptive gradient gain allocation and alleviate the negative impact of low-quality samples on the gradient. The simulation results reveal that the proposed algorithm has achieved a favorable average accuracy of 86.3\% in real-time scene detection tasks. This marks a significant enhancement of 6.3\% compared to the baseline, indicating the algorithm's good performance for floating waste detection.