Abstract:Infrared dim and small target detection technology is one of the core technologies of infrared detection systems. Aiming at the low target detection rate caused by low contrast, low signal-to-noise ratio, and sparse texture features in remote complex scenes, a fusion of attention mechanism and improved YOLOv3 infrared dim target detection algorithm is proposed. First, based on YOLOv3, the smallest detection head is replaced with a larger detection head, which can effectively improve the detection probability of small targets in infrared images with ensuring the inference speed as much as possible. Then, the Infrared Attention module is added before the detection head to realize the information exchange between channels and focus on the important target features. Finally, use Complete IoU Lossinstead of Intersection Over Union Loss to achieve better training of the model. The experimental results show that the YOLOv3 DCA proposed can complete the detection of infrared small targets in various scenarios. The accuracy, recall, F1, and average accuracy of detecting infrared dim small targets by the optimized YOLOv3 algorithm are 91.84%, 88.85%, 93%, and 88.82%, respectively. The average accuracy is about 7% higher than the YOLOv3 baseline, and the average accuracy is currently the best compared to the SSD, CenterNet, and YOLOv4 models.