| Summary: | Aiming at the problems of low detection accuracy and low real-time performance in the detection and recognition of mine moving targets in foggy and low-illumination environment, a method based on deep convolutional neural network for intelligent detection and recognition of mine moving targets is proposed. The visual sensor is used to capture a frame of the underground mine scene to construct the environment model. The original image of the moving target is used as the model input, and the digital identifier is embedded in the specific position of the moving target image, which creates a data set containing the digital sequence position information. A novel off-line training model named single shot multibox detector(SSD)is presented, which can output target feature categories corresponding to its position. Then, the trained SSD learning model is used to detect the position of the digital sequence on the moving target image in the test set, and characters are split according to the rectangular region corresponding to the position of the digital sequence. Furthermore, the segmented single characters are put into the LeNet-5 network for sequential recognition The recognized single characters are sequentially combined into a digital sequence, thereby quickly retrieving the identity information of the mobile target. The research shows that compared with other target detection and recognition methods, the proposed method has higher accuracy and robustness for target detection and recognition under low-illumination and noisy environment, and can meet real-time requirements.
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