CHL-YOLO: A Lightweight Detector for Complex SAR Ship Detection

Abstract

Synthetic aperture radar (SAR) ship detection in complex maritime scenes is challenged by speckle noise, sea clutter, coastal interference, weak small-target responses, and large-scale variations. To address these problems while maintaining low computational complexity, this article proposes CHL-YOLO, a lightweight detector based on YOLOv11n. The name CHL-YOLO corresponds to its three principal improvements: the Convolutional Gated Linear Unit (CGLU), the Hybrid-Scale Feature Pyramid Network (HSFPN), and Localization Quality Estimation (LQE). First, a C2PSA_CGLU module is introduced into the backbone to enhance spatial–channel feature representation and dynamically suppress redundant background responses. Second, HSFPN performs channel-wise feature selection before cross-scale aggregation and uses high-level semantic features to generate input-dependent gates for low-level spatial details, thereby reducing the repeated propagation of sea clutter. Third, the LQE branch estimates bounding-box localization reliability from discrete boundary distributions and combines the quality score with classification confidence during non-maximum suppression. Experiments on SSDD and HRSID demonstrate that CHL-YOLO contains only 1.83 M parameters and requires 5.1 GFLOPs, corresponding to reductions of 33.0% and 22.7%, respectively, compared with YOLOv11n. On SSDD, mAP@0.5:0.95 increases from 62.6% to 67.6%, while on HRSID it increases from 62.7% to 64.7%. The proposed model achieves inference speeds of 68.82 FPS and 67.58 FPS on the two datasets, respectively. These results demonstrate that CHL-YOLO achieves a favorable balance among detection accuracy, model complexity, and real-time inference for complex SAR ship detection.