Tooth Guard: A Vision System for Detecting Missing Tooth in Rope Mine Shovel

Abstract

Rope shovels are widely used in the mining industry to dig ore. During operation, one or more teeth in the bucket can be lost as a result of the force that impacts the teeth, which causes a serious problem when the broken teeth gets picked up by the haul truck and eventually ended up in the crusher and jamming it. In this paper, we present a vision system for monitoring tooth condition and detecting missing tooth for a mining shovel. Our system leverages the predictable range of motion that the bucket of a rope shovel goes through during operation due to the camera mounting. For this reason, our strategy is to use exemplar based image retrieval and a sliding window procedure to first locate a pre-defined static region of the bucket in a live frame, followed by detecting the tooth line region based on its relative position to the selected exemplar. Once the tooth line region is detected, we proceed by determining its transformation from a recently detected tooth line region, and rectify the tooth line region before conducting image differencing. The difference image is then converted into a response map by sliding a tooth template associated with the retrieved exemplar and computing a correlation score per sliding window. Finally, the tooth line associated with the retrieved exemplar is rigidly scaled and rotated within a specified range in the response map to find a final tooth line position with the largest overall fitting score. An individual tooth is then flagged as missing if its missing measurement exceeds a threshold. The outstanding performance and high reliability of the proposed system have been demonstrated by experiments on video sequences collected from an iron ore mining site and a two-month trial of an installed unit on a production line.