A small moving object detection method based on attention mechanism and location analysis from infrared images

Infrared images have the advantage of revealing thermal signatures, enabling enhanced visibility and detection of objects, especially in low-light conditions or scenarios where conventional cameras may struggle. However, detecting small moving objects, such as cars, drones, and people, in infrared images is a challenging task affected by ground clutter and complex image backgrounds, which increases more false positives in detection results. To relieve this problem, a twostage moving object detection method is proposed in this paper. Firstly, a newly designed attention mechanism is inserted into the feature extraction backbone for the YOLOv8 model, which helps the detection framework focus on the object region to reduce interference from complex image backgrounds and increase the detection rate. Secondly, an FP reduction strategy is proposed based on object moving analysis, which associates the detection results in the previous frame of the video with the current frame and performs target motion pattern analysis, and FPs that do not conform to the movement patterns are removed. Experimental results show that the proposed method is beneficial to not only reducing the FPs but also increasing the detection rate.