Object detection technology has always been one of the important research directions in the field of computer vision. It has important application value and prospects in both civil fields and military fields. With the emergence of artificial intelligence technology, deep learning has gradually replaced the traditional algorithm with its higher accuracy. Considering that most of the current algorithms are for color image, compared with color image, infrared image contains less feature information, and it is more difficult in object detection. In this paper, different algorithms are used for object detection in infrared images, and the detection results are compared. This paper chooses YOLO V5 and combines it with MobileNet to lighten the model. After lightening, the parameters are reduced by 30%, but the accuracy is only reduced by 5%. Finally, this paper quantify YOLO V5 based on the model quantization method of PyTorch. After quantization, the accuracy of the model decreases by 2%.
In order to solve the problem of small data set, this paper uses the invariance of distinguishing features between the simulated infrared image of maritime ship and real infrared image of maritime ship, studies a method of detecting infrared maritime ship target with no real data. At the same time, we propose an attribute adaptive learning strategy based on deep learning algorithm of yolov3. In the case of low data support, the detection capabilities of infrared maritime ship target have been improved.
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