Infrared Ship Target Detection Based on Improved YOLOv7-tiny

An improved YOLOv7-tiny-based lightweight infrared ship target detection model is proposed to address the issues of low accuracy and high computation load of ship image detection in the infrared range. First, the lightweight model PP-LCNet is employed in the backbone network, which significantly reduces both the number of parameters and computational requirements. Second, an improved Fused-MBConv module and a Coordinate Attention (CA) mechanism are incorporated to construct the ELAN-FM-C module, which is then integrated into the feature fusion layer to comprehensively focus on the spatial and channel information of the feature layer to obtain a large receptive field. Subsequently, the Minimum Distance Points Intersection over Union (MDPIoU) loss function, which compares the bounding box similarity based on the minimum point distance, is adopted to simplify the computation process and improve the detection capability of the lightweight model for infrared targets. Based on this, an R-BiFPN structure is proposed to fuse more effective features, thereby improving the detection performance of the lightweight model across targets of different scales. Finally, a knowledge distillation technique is used to further improve the detection accuracy of the model. The improved model is validated on the Iray Optoelectronics infrared offshore ship dataset, achieving a mean Average Precision (mAP) that is 3.3 percentage points higher than that obtained using the original YOLOv7-tiny model. Simultaneously, the parameter and computational complexities are reduced by 23.0% and 30.3%, respectively, and the model size is reduced by 21.7%. Experiments on publicly available ship datasets, namely SeaShips and Ship Images, reveal that, compared to other mainstream and latest detection models, the improved model demonstrates excellent generalization and robustness and outperforms other models in terms of both detection accuracy and lightweight design.