| Summary: | With the rapid expansion of global maritime transportation, infrared ship detection has become increasingly critical for ensuring navigational safety, enhancing maritime monitoring, and supporting environmental protection. To address the limitations of conventional methods in handling small-scale targets and complex background interference, in this paper, we propose an improved approach by embedding the convolutional block attention module (CBAM) into different components of the YOLOv5 architecture. Specifically, three enhanced models are constructed: the YOLOv5n-H (CBAM embedded in the head), the YOLOv5n-N (CBAM embedded in the neck), and the YOLOv5n-HN (CBAM embedded in both the neck and head). The comprehensive experiments are conducted on a publicly available infrared ship dataset to evaluate the impact of attention placement on detection performance. The results demonstrate that the YOLOv5n-HN achieves the best overall performance, attaining the mAP@0.5 of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>86.83</mn><mo>%</mo></mrow></semantics></math></inline-formula>, significantly improving the detection of medium- and large-scale maritime targets. The YOLOv5n-N exhibits superior performance for small-scale target detection. Furthermore, the incorporation of the attention mechanism substantially enhances the model’s robustness against background clutter and its discriminative capacity. This work offers practical guidance for the development of lightweight and robust infrared ship detection models.
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