YOLO-GEA: infrared target detection algorithm based on multi-scale feature fusion

Abstract In infrared (IR) target detection, achieving reliable detection results at high speed is essential. To address the problems of low accuracy, large number of parameters, and complexity of the target detection model for IR images in complex backgrounds, it is difficult to achieve a better balance between accuracy and speed, we propose a YOLO-GCSPNet-efficient dual attention mechanism (EDAM) Attention (YOLO-GEA) IR target detection algorithm. Firstly, to significantly reduce model parameters while maintaining detection accuracy, we designed a lightweight partial multi-scale feature aggregation module (CSP-PMFA). Secondly, we proposed an EDAM, which adaptively learns the importance of each channel and spatial position, thereby better capturing key information in the image. Additionally, we developed the GCSPNet backbone network based on GhostNet and the CSP-PMFA module to optimize the feature aggregation process and achieve model compression. Finally, the WIoUv3 loss function is employed to further improve the precision of bounding box regression. Experiments demonstrate that on the IFdata and FLIR datasets, the mAP@0.5 reached 90.1% and 86.0%, respectively, representing improvements of 3.4% and 1.5% compared to the original YOLOv8, while reducing the number of parameters by 13.0%. Additionally, on the NEU-DET dataset, the mAP@0.5 achieved 79.8%, validating the model’s generalization performance across different datasets.