YOLO-infrared: Enhancing YOLOX for Infrared Scene

Draw a bead on the specialty of infrared scenes and tackling the disequilibrium between positive and negative samples in object detectors, this paper introduces an object detection model for infrared scenes named YOLO-infrared based on YOLOX. This paper first analyses the shortcomings of the object detection model designed for the visible domain when applied to the infrared domain by visualizing the feature heat map of the YOLOX neck network. Considering the blurred edges of the target in the infrared image, which almost blends with the background in terms of colour and texture, with little distinction between pixel points at close distances, this paper first employs an attention module to extract the position relationship between distant pixels, which enhances the feature abstracting capacity of YOLOX. Finally, DR Loss is utilized to address the issue of positive and negative sample imbalance in the object detection process. The model YOLO-infrared proposed in this paper achieves 38.2% and 38.6% on the FLIR and KAIST datasets, respectively, which is at least 0.5% higher than the current SOAT detector.