GS-BiFPN-YOLO: A Lightweight and Efficient Method for Segmenting Cotton Leaves in the Field

Instance segmentation of cotton leaves in complex field environments presents challenges including low accuracy, high computational complexity, and costly data annotation. This paper presents GS-BiFPN-YOLO, a lightweight instance segmentation method that integrates SAM for semi-automatic labeling and enhances YOLOv11n-seg with GSConv, BiFPN, and CBAMs to reduce annotation cost and improve accuracy. To streamline parameters, the YOLOv11-seg architecture incorporates the lightweight GSConv module, utilizing group convolution and channel shuffle. Integration of a Bidirectional Feature Pyramid Network (BiFPN) enhances multi-scale feature fusion, while a Convolutional Block Attention Module (CBAM) boosts discriminative focus on leaf regions through dual-channel and spatial attention mechanisms. Experimental results on a self-built cotton leaf dataset reveal that GS-BiFPN-YOLO achieves a bounding box and mask mAP@0.5 of 0.988 and a recall of 0.972, maintaining a computational cost of 9.0 GFLOPs and achieving an inference speed of 322 FPS. In comparison to other lightweight models (YOLOv8n-seg to YOLOv12n-seg), the proposed approach achieves superior segmentation accuracy while preserving high real-time performance. This research offers a practical solution for precise and efficient cotton leaf instance segmentation, thereby facilitating the advancement of intelligent monitoring systems for cotton production.