Abstract
Deep learning is a problem in the field of infrared target detection. The main reason is that infrared data has less information. Therefore, traditional detection algorithms have lower detection speed and detection accuracy. In this paper, we mainly propose a fast detection algorithm based on YOLOv3. Due to the real-time nature of YOLOv3 needs to be improved, we merged the improved Mobilenetv2 and YOLOv3 to get a new network structure. Experiments on the standard target detection data set show that the network parameters of the new network are only 30% of the original network, and the detection speed is increased by 3 times. At the same time, the mAP is also improved. Through the experiments on the infrared dataset, we get the following test results: the detection speed of the new constructed network structure can reach 11ms/f, and in good weather conditions, mAP can also reach 76%. This indicates that the network structure proposed in this paper can achieve better results in the fast recognition of infrared.
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