Target identification and precise positioning method based on underground moving image collectio
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摘要: 针对现有井下定位方法定位精度波动较大、难以进一步提高的问题，提出一种基于井下移动图像采集的目标识别与精确定位方法。利用定位目标携带的摄像机采集环境图像，通过自适应直方图均衡化方法对采集到的原始图像进行预处理，采用深度学习技术SSD算法、数据增强SSD算法识别井下标志目标，并采用基于小孔成像原理的单目测距方法进行测距和定位。实验结果表明：与灰度图像匹配算法和特征图像匹配算法2种传统算法相比，SSD算法对距离和角度变化的适应能力更好，距离为45 m时有效检测率仍达892%；数据增强SSD算法提高了鲁棒性，检测精确率比SSD算法高17%，可以更好地适应复杂环境。井下应用结果表明，基于井下移动图像采集的目标识别与精确定位方法在2～10 m范围内可得到较理想的效果，随着距离增加，测量精度有所下降。Abstract: The positioning accuracy of the existing underground positioning method fluctuates greatly and is difficult to be further improved. In order to solve the problem, a target identification and precise positioning method based on underground moving image collection is proposed. The environmental images are collected by using the camera carried by the positioning target, and the collected raw images are pre-processed by the adaptive histogram equalization method. The deep learning technology SSD algorithm and data enhancement SSD algorithm are used to identify the underground mark target, and the monocular distance measuring method based on the pinhole imaging principle is applied for ranging and positioning. The experimental results show that compared with two traditional algorithms of gray image matching algorithm and characteristic image matching algorithm, the SSD algorithm has better adaptability to distance and angle changes, and the effective detection rate still reaches 89.2% at 4.5 m. The data enhancement SSD algorithm improves the robustness and the detection accuracy rate is 1.7% higher than that of the SSD algorithm. The algorithm can better adapt to the complex environment. The results of underground application show that the target identification and precise positioning method based on underground moving image collection can achieve satisfactory results in the range of 2-10 m. The measurement accuracy decreases as the distance increases.
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