A small object detection method based on the attention mechanism and multi-level feature fusion

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.