Edge-global collaborative enhancement detection for low-contrast infrared sea ice images

Infrared imaging serves as a core sensing modality for sea ice monitoring under severe weather conditions, owing to its excellent fog-penetration capability that supports continuous observation. However, low contrast and blurred edges remain key challenges in infrared sea ice imagery.

This paper proposes an edge-global collaborative enhancement detection method built upon the YOLOv7 (You Only Look Once version 7) framework. A Sobel contour extraction module is embedded in the shallow ELAN blocks, which extracts horizontal and vertical gradients and performs adaptive fusion to enhance the edge gradient features of sea ice boundaries. A Large Kernel Convolution (LKC) module is then added to the deep ELAN blocks, adopting a dual-branch parallel complementary design: the main bottleneck branch expands the network's receptive field, while the short-connected 3×3 convolution branch preserves fine local details of targets and correlates local image patches with the global radiative background.

Experiments are conducted on a multi-weather infrared sea ice dataset, with the original YOLOv7 set as the baseline for comparison. Three control models are constructed: Sobel-only, LKC-only, and dual-module. Four metrics are adopted for evaluation: precision, recall, mAP@0.5, and computational cost. Under dense fog conditions, the proposed method achieves a recall of 0.674 and an mAP@0.5 of 0.671, corresponding to improvements of 9.3 and 7.7 percentage points over the baseline, respectively. On the mixed-weather test set, recall reaches 0.63 and mAP@0.5 reaches 0.627. Ablation experiments verify that the dual-module model delivers the best performance across all metrics, with only a slight increase in computational cost from 103.2 GFLOPs to 103.5 GFLOPs.

By integrating edge enhancement and global background modeling, the proposed method significantly improves the detection accuracy of low-contrast sea ice targets and enhances model robustness in harsh weather scenarios, and can effectively meet the requirements of real-time sea ice monitoring under adverse weather conditions.