Research on Infrared Target Detection and Tracking in Dark Environments

The efficient and accurate detection and tracking of dynamic target features in dark environments, where contours are blurred and occlusions are present, hold practical significance for disaster relief, search and tracking operations. To effectively detect and track the blurred contour features in dark environments, an improved real-time infrared target tracking and detection algorithm is proposed. This algorithm, based on the deep learning network (Spatial Local Dynamic You Only Look Once, SLD-YOLOv8), incorporates a non-local adaptive module and a spatial channel convolution (SCC) correlation module to optimize the Bottleneck CSP of YOLOv8 network for better feature extraction. A dedicated 160×160 detection layer and a dynamic head are introduced for the improved detection of small-scale targets and the enhanced boundary regression capabilities in low-light scenarios, enabling the accurate real-time inference of relative target position. Experimental validation shows that the proposed algorithm has good robustness and accuracy in detecting the dynamic features in dark environments. The average precision evaluation metrics mAP_0.5 and mAP_0.5:0.95 of this model are increased by 5.6% and 4.5%, respectively, compared to the original model, affirming its effectiveness of tracking the targets in dark environments.