Development and Application of an Intelligent Recognition System for Polar Environmental Targets Based on the YOLO Algorithm

As global climate warming enhances the navigability of Arctic routes, their navigation value has become prominent, yet ships operating in ice-covered waters face severe threats from sea ice and icebergs. Existing manual observation and radar monitoring remain limited, highlighting an urgent need for efficient target recognition technology. This study focuses on polar environmental target detection by constructing a polar dataset with 1342 JPG images covering four classes, including sea ice, icebergs, ice channels, and ships, obtained via web collection and video frame extraction. The “Grounding DINO pre-annotation + LabelImg manual fine-tuning” strategy is employed to improve annotation efficiency and accuracy, with data augmentation further enhancing dataset diversity. After comparing YOLOv5n, YOLOv8n, and YOLOv11n, YOLOv8n is selected as the baseline model and improved by introducing the CBAM/SE attention mechanism, SCConv/AKConv convolutions, and BiFPN network. Among these models, the improved YOLOv8n + SCConv achieves the best in polar target detection, with a mean average precision (mAP) of 0.844–1.4% higher than the original model. It effectively reduces missed detections of sea ice and icebergs, thereby enhancing adaptability to complex polar environments. The experimental results demonstrate that the improved model exhibits good robustness in images of varying resolutions, scenes with water surface reflections, and AI-generated images. In addition, a visual GUI with image/video detection functions was developed to support real-time monitoring and result visualization. This research provides essential technical support for safe navigation in ice-covered waters, polar resource exploration, and scientific activities.