Infrared Target Detection Based on the Fusion of Attention Mechanism and YOLOv5

Infrared images have the characteristics of strong anti-interference ability and all-weather work, and are used in many fields. This paper improves the infrared target detection based on the YOLOv5 deep learning algorithm. Since the attention mechanism has the function of ignoring irrelevant information and focusing on key information, this paper integrates the attention mechanism with the YOLOv5 algorithm to improve the feature extraction ability of the algorithm. Increase the multi-scale detection strategy; this paper conducts experiments on the public infrared data set. The test results show that the YOLOv5 algorithm based on the fusion of attention mechanism and multi-scale detection improves the target detection accuracy by 3.2%, and also has good performance in small infrared target detection.