Infrared target detection based on improved YOLOv5
Aiming at the problems of insufficient accuracy of infrared target detection caused by differences in infrared imaging characteristics and their application environments,this paper proposes an infrared target detection method based on the improved YOLOv5 algorithm.The method introduces the SIoU loss function and the multi-scale expansion attention mechanism to enhance the model's detection performance for small-size targets and targets in complex backgrounds.The experimental results show that compared with the unimproved YOLOv5 model,the improved model has significant improvement in key performance indicators such as precision rate,recall rate and average precision rate,which proves the effectiveness of the improved method.
infrared target detectionSIoU loss functionmultiscale dilated attention
万方数据
维普
