CDT-YOLO: A Long-term Detection and Tracking Algorithm for Infrared Moving Small Objects in Missile-Borne Platforms

To improve the accuracy and speed of missile detection and tracking for infrared moving small objects (IMSO), a continuous detection and tracking algorithm called CDT-YOLO is proposed. The algorithm mainly consists of an object detection module based on an improved YOLOv8, a tracking confidence evaluation module, and a multi-scale tracking module based on template matching. The object detection module has demonstrated excellent detection performance for IMSO in complex backgrounds. The YOLOv8 model introduces a lightweight convolution known as GSConv to balance detection accuracy and speed. Additionally, the model uses Wise-IoU (WIoU) v3 as a bounding box regression loss to improve localization ability. The object tracking module shows high robustness to size transformation and rapid movement of small objects. The tracking confidence evaluation module is able to re-invoke the detection algorithm to resume tracking the object when the tracking status is found to be abnormal. After experimental validation, the inference speed of the object detection algorithm exceeds 134 FPS with precision up to 0.842 and AP50 up to 0.692. The CDT-YOLO algorithm outperforms classic tracking algorithms in terms of tracking success rate (Sr60) up to 0.774 and speed more than 290 FPS. The method also has the capability of long-term tracking and can be operated in real-time on the missile platform.