Infrared imaging technology has become indispensable across numerous fields due to its unique capabilities. However, small object detection from infrared images is still a challenging task because of the inherent noise and low contrast often present in infrared imagery, and the small target size further increases the detection difficulty. To relieve these problems, we propose a small object detection method based on YOLOv8 with an attention mechanism and multi-level feature fusion from infrared images. Firstly, an attention mechanism is introduced to extract long-range image features, which is effective in decreasing the bad effect of complex image backgrounds. Secondly, multi-level feature fusion is used in the detection neck to recover image details for small objects. Experimental results show that the proposed method is beneficial in improving the detection performance of small objects 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.
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