Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang 110169, China.
Liaoning Huading Technology Co.Ltd, Shenyang, Liaoning 110167, China.
Comput Intell Neurosci. 2021 Nov 22;2021:6503029. doi: 10.1155/2021/6503029. eCollection 2021.
Electric shovels are widely used in the mining industry to dig ore, and the teeth in shovels' bucket can be lost due to the tremendous pressure exerted by ore materials during operation. When the teeth fall off and enter the crusher with other ore materials, serious damages to crusher gears and other equipment happen, which causes millions of economic loss, because it is made of high-manganese steel. Thus, it is urgent to develop an efficient and automatic algorithm for detecting broken teeth. However, existing methods for detecting broken teeth have little effect and most research studies depended on sensor skills, which will be disturbed by closed cavity in shovel and not stable in practice. In this paper, we present an intelligent computer vision system for monitoring teeth condition and detecting missing teeth. Since the pixel-level algorithm is carried out, the amount of calculation should be reduced to improve the superiority of the algorithm. To release computational pressure of subsequent work, salient detection based on deep learning is proposed for extracting the key frame images from video flow taken by the camera installed on the shovel including the teeth we intend to analyze. Additionally, in order to more efficiently monitor teeth condition and detect missing teeth, semantic segmentation based on deep learning is processed to get the relative position of the teeth in the image. Once semantic segmentation is done, floating images containing the shape of teeth are obtained. Then, to detect missing teeth effectively, image registration is proposed. Finally, the result of image registration shows whether teeth are missing or not, and the system will immediately alert staff to check the shovel when teeth fall off. Through sufficient experiments, statistical result had demonstrated superiority of our presented model that serves more promising prospect in mining industry.
电动铲广泛应用于矿业挖掘矿石，在操作过程中，铲斗中的齿会受到矿石的巨大压力而脱落。当齿脱落并与其他矿石材料进入破碎机时，会对破碎机齿轮和其他设备造成严重损坏，因为破碎机齿轮和其他设备是用高锰钢制成的，这会造成数百万美元的经济损失。因此，迫切需要开发一种高效、自动的断齿检测算法。然而，现有的断齿检测方法效果不佳，大多数研究依赖于传感器技术，而这些技术会受到铲斗内部封闭空间的干扰，在实际应用中不稳定。在本文中，我们提出了一种用于监测齿状态和检测缺失齿的智能计算机视觉系统。由于进行了像素级算法，因此应减少计算量以提高算法的优越性。为了释放后续工作的计算压力，提出了基于深度学习的显著检测，从安装在铲斗上的摄像机拍摄的视频流中提取关键帧图像，包括我们要分析的齿。此外，为了更有效地监测齿状态和检测缺失齿，对基于深度学习的语义分割进行处理，以获取图像中齿的相对位置。一旦完成语义分割，就会获得包含齿形状的浮动图像。然后，为了有效地检测缺失齿，提出了图像配准。最后，图像配准的结果显示是否有齿缺失，并且系统会在齿脱落时立即提醒工作人员检查铲斗。通过充分的实验，统计结果表明了我们提出的模型的优越性，在采矿业中有更广阔的前景。
