LA‐YOLO: Location Refinement and Adjacent Feature Fusion‐Based Infrared Small Target Detection

In the field of infrared small target detection (ISTD), the ability to detect targets in dim environments is critical, as it improves the performance of target recognition in nighttime and harsh weather conditions. The blurry contour, small size and sparse distribution of infrared small targets increase the difficulty of identifying such targets in cluttered backgrounds. Existing methodologies fall short of satisfying the requisites for the detection and categorisation of infrared small targets. To address these challenges and to enhance the precision of small object detection and classification, this paper introduces an innovative approach called location refinement and adjacent feature fusion YOLO (LA‐YOLO), which enhances feature extraction by integrating a multi‐head self‐attention mechanism (MSA). We have improved the feature fusion method to merge adjacent features, to enhance information utilisation in the path aggregation network (PAN). Lastly, we introduce supervision on the target centre points in the detection network. Empirical results on publicly available datasets demonstrate that LA‐YOLO achieves an impressive average precision (AP) of 92.46% on IST‐A and a mean average precision (mAP) of 84.82% on FLIR. The results surpass those of contemporary state‐of‐the‐art detectors, striking a balance between precision and speed. LA‐YOLO emerges as a viable and efficacious solution for ISTD, making a substantial contribution to the progression of infrared imagery analysis. The code is available at https://github.com/liusjo/LA‐YOLO .