Research on Ship Personnel Detection Method in Complex Scenarios Based on Fusion of Attention Mechanism and Multi-Scale Perception

Ship personnel detection is the core technology for building intelligent ship monitoring systems. It safeguards navigation safety and standardizes deck work management. Current human detection algorithms perform poorly in complex marine service environments. They are easily disrupted by interfering backgrounds: blocked personnel, stacked deck equipment, and sea surface reflections. In addition, these algorithms struggle to detect tiny distant targets. Such drawbacks drastically reduce detection accuracy and stability. As a result, they cannot satisfy real-world needs for marine navigation and offshore on-site operations. To tackle the aforementioned challenges, this paper proposes a ship personnel detection method for complex scenarios based on attention mechanism and multi-scale perception fusion. By optimizing the small-target detection branch, the proposed method strengthens the capability to capture and identify long-distance operators on ship decks. Furthermore, a convolution–attention fusion module is embedded into the backbone network of the model to effectively separate personnel features from the complex marine background. In addition, depthwise separable convolution is introduced to replace conventional standard convolution, which substantially reduces model computational complexity and enables the model to meet the real-time detection requirements of ship application scenarios. Experimental results show that the improved model achieves an accuracy of 93% in tests across various ship deck scenarios. Compared with the original YOLOv8 model, its precision and recall are improved by 3% and 2%, respectively. The comprehensive performance of the proposed model is superior to that of mainstream object detection models such as Faster-RCNN and YOLOv5. It provides an efficient and reliable technical solution for personnel safety monitoring and operation management in complex ship scenarios.