Infrared small-scale pedestrian detection based on YOLO

Infrared small-scale pedestrian detection is particularly important at night and under low-light conditions, but it also faces great challenges, such as the small size of pedestrians and unclear target features. In order to solve these problems, this paper introduces two attention mechanisms based on the YOLOv5s model: channel attention mechanism (CA) and efficient channel attention mechanism (ECA) to enhance the model's ability to identify infrared pedestrian targets. By integrating these attention mechanisms in the YOLOv5s model, this paper aims to improve the accuracy and robustness of infrared pedestrian detection. In addition, this study also specially created an infrared pedestrian image dataset to train and test the proposed optimization model, and verified the effectiveness of the model through ablation experiments. Experimental results show that compared with the original YOLOv5s model, the optimized model achieved an improvement of 1.9 percentage points in average accuracy (AP), reaching 94.1%, which proves the effectiveness of the proposed method and its performance in infrared pedestrian detection tasks adaptability.