Infrared target detection algorithm based on multipath coordinate attention mechanism

Abstract

The current generation of infrared target detection algorithms frequently exhibits a high degree of dependency on parameter configurations within complex operational environments. This often results in a reduction in detection accuracy, an increase in the number of model parameters, and a slowing of the detection process. To address these limitations, a new algorithm, CGhostNet-Attention-YOLO (CAY), is proposed in this paper. Firstly, we designed a lightweight backbone network, CGhostNet, with the objective of improving feature extraction efficiency, thereby enabling accurate and real-time feature extraction. Furthermore, we proposed a Multipath Coordinate Attention (MPCA) mechanism, which incorporates both channel and positional information, thereby facilitating enhanced context awareness and the comprehension of relationships between different positions. This effectively enhances the model's ability to comprehend the overall meaning and addresses the issue of missed detections in infrared targets, significantly improving detection accuracy. Moreover, we employed the Inner-SIoU loss function to accelerate model convergence, reduce loss, and enhance the robustness of the model. Finally, comparative experiments were conducted on our dataset (IFD) as well as publicly available datasets, including FLIR, Pascal VOC, and NEU-DET. The results demonstrate that the CAY algorithm achieved a mean Average Precision (mAP@0.5) of 81.3% on the IFD dataset, 86.1% on the FLIR dataset, 79.2% on the Pascal VOC dataset, and 79.9% on the NEU-DET dataset, with a 27% reduction in the number of parameters. These findings validate the feasibility of the proposed algorithm.