Research on Lightweight Scenic Area Detection Algorithm Based on Small Targets

Given the difficulty of effectively detecting small target objects using traditional detection technology in current scenic waste disposal settings, this paper proposes an improved detection algorithm based on YOLOv8n deployed on mobile carts. Firstly, the C2f-MS (Middle Spilt) module is proposed to replace the convolution module of the backbone network. Retaining the original feature details of different scales enhances the ability to detect small targets while reducing the number of model parameters. Secondly, the neck network is redesigned, introducing the CEPN (Convergence-Expansion Pyramid Network) to enhance the semantic feature information during transmission. This improves the capture of detailed information about small targets, enabling effective detection. Finally, a QS-Dot-IoU hybrid loss function is proposed. This loss function enhances sensitivity to target shape, simultaneously focuses on classification and localization, improves the detection performance of small targets, and reduces the occurrence of false detections. Experimental results demonstrate that the proposed algorithm outperforms other detection algorithms regarding small targets' detection performance while maintaining a more compact size.