Robust Ship Detection Algorithm Under Complex Occlusion Conditions
TL;DR
The proposed OAR-YOLO model has only 2.89 M parameters and 5.7 GFLOPs, with an inference time of 0.8 ms per frame, meeting the real-time deployment requirements of complex maritime applications.
Abstract
To address the accuracy degradation of ship detection caused by occlusion from adjacent vessels, shore-based facilities and meteorological obscuration in complex maritime-surveillance scenes, this paper proposes an occlusion-robust detection model named OAR-YOLO. An adaptive dual-path downsampling module termed ADown was embedded at the three backbone levels P3, P4 and P5, in which low-frequency contextual information and high-frequency edge information were preserved separately through parallel average-pooling and max-pooling branches, alleviating the information loss caused by conventional strided-convolution downsampling. An attention-driven intra-scale feature interaction module termed AIFI was embedded at the top level P5 to establish semantic associations between spatially separated visible regions through global self-attention, compensating for the insufficient cross-region connectivity caused by the locality of convolution. The two modules formed a dual compensation mechanism of information conservation and semantic connectivity. On a self-built ship dataset, OAR-YOLO achieved a Precision of 81.0%, an mAP@0.5 of 74.7% and an mAP@0.5–0.95 of 47.1%, with gains of 2.7, 2.6 and 1.4 percentage points over the YOLO11n baseline. The model has only 2.89 M parameters and 5.7 GFLOPs, with an inference time of 0.8 ms per frame, meeting the real-time deployment requirements of complex maritime applications.