SSD-LeNet based method of mine moving target detection and recognition
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摘要: 针对井下雾尘、低照度环境中矿井移动目标检测与识别存在检测精度低、实时性差等问题，提出了一种基于SSD-LeNet的矿井移动目标检测与识别方法。利用视觉传感器捕获矿井移动目标原始图像的一帧来构建模型输入，据此制作含有数字序列位置信息的数据集；离线训练的单镜头多盒检测器(Single Shot multibox Detector，SSD)模型可以输出与自身位置对应的目标特征类别，并利用该训练好的SSD学习模型对测试集中移动目标图片上的数字序列位置进行检测；根据数字序列位置对应的矩形区域进行字符分割操作，将分割后的单个字符依次放入LeNet网络中进行特征识别；识别出的单个字符按顺序合成数字序列快速检索出移动目标的身份信息。研究表明，本文方法与其他深度学习目标检测与识别方法相比，对矿井低照度及噪声环境下的目标检测与识别具有较高的准确率和较强鲁棒性，能够满足实时性要求。Abstract: 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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图 2 矿井移动目标检测与识别总体流程
Figure 2. Process of mine moving target detection and tracking identification
图 6 场景一的移动目标检测效果及置信度
Figure 6. Example diagram of moving target detection effect and confidence
图 7 场景二的移动目标检测效果及置信度
Figure 7. Example diagram of moving target detection effect and confidence
图 8 场景三的移动目标检测效果及置信度
Figure 8. Example diagram of moving target detection effect and confidence
表 1 几种方法在不同噪声条件下的检测准确率和实时性比较
Table 1. Comparison of real-time and precision of the algorithm in different image noise
噪声 方法 实时性/(帧·s-1) 检测精度/% 数字标签 人员 平均 理想条件 Fast R-CNN 0.5 88.1 88.3 88.2 Faster R-CNN 7.0 90.2 90.6 90.4 YOLO 21.0 83.0 83.5 83.2 SSD300 32.5 92.8 95.8 94.3 高斯噪声 Fast R-CNN 0.5 88.0 88.3 88.1 Faster R-CNN 7.0 90.0 90.4 90.2 YOLO 21.0 82.7 83.3 83.0 SSD300 32.0 92.5 95.6 94.1 椒盐噪声 Fast R-CNN 0.5 88.1 88.1 88.2 Faster R-CNN 8.0 89.8 90.3 90.1 YOLO 21.0 82.6 82.9 82.7 SSD300 32.0 92.4 95.6 94.0 
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