Abstract
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.
Highlights
Shovel excavators are widely used as primary production equipment in surface mining for removing overburden and ore materials due to their flexibility and mobility [1]. ey dig the work front and load mining trucks in a three- or fourpass loading cycle. e shovel consists of three major assemblies: lower frame, upper frame, and attachment bucket [2], which is responsible for digging up the payload to the trucks. e bucket tooth of the loader is an important component of the loader structure [3], as well as a vulnerable part; the mounting part of the bucket tooth is at the front end of the bucket in order to reduce friction [4]
E first sets of experiments are conducted to delete images without teeth to reduce the amount of computation, and the second sets of experiments are conducted to find the region of interest (ROI) and the location of teeth in the images
It should be noticed that multiple dilation rates are proposed to extract multisemantic information, and the dilated convolution neural network is contracted to a normal convolution to demonstrate that the DeepLabV3+ model works more effectively. ird, image registration is conducted to detect whether the teeth are missing or not by counting the number of rectangles
Summary
Received 30 August 2021; Revised 17 September 2021; Accepted 29 September 2021; Published 22 November 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. 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. 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. En, to detect missing teeth effectively, image registration is proposed. 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. 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. rough sufficient experiments, statistical result had demonstrated superiority of our presented model that serves more promising prospect in mining industry
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