Research on Polar Environment Target Detection and Intelligent Recognition System Based on Lightweight YOLO Dual-Path Optimization

With the melting of Arctic sea ice and extended navigable windows, polar navigation has gained prominent commercial and strategic value but faces challenges like strong ice reflection, high target texture similarity, and large obstacle scale variation. Aiming at scarce polar-specific datasets, poor adaptability of general algorithms, and disconnection between identification and navigation decisions, this study constructed a technical system integrating “dataset construction–algorithm improvement–system development”. A purpose-built polar dataset with 1342 images (covering drift ice, iceberg, ice channel, and ship) was built via web crawling, video frame extraction, and data augmentation. A dual-path optimization scheme for lightweight YOLO models was proposed: the YUV + CLAHE module suppresses strong reflection, and the IceTextureAttention module enhances discriminability of similar targets, with SCConv optimizing computational efficiency. A visual intelligent system embedded with a Polar Code-based risk assessment module was developed to output three-level risks and navigation suggestions. Experimental results show the optimized YOLOv8n + YUV + CLAHE model achieves an overall mAP@0.5 of 0.858 and a recall rate of 0.821. The system runs stably on shipborne equipment with an average image processing latency of 85 ms and a practical detection accuracy of 84.3%, effectively reducing crew workload and improving polar navigation safety.