Improving Mining Shovel Tooth Failure Detection Using Computer Vision-Based Methodologies
The current computer vision-based methods for identifying broken teeth on mining shovels suffer from a prohibitively high false-positive rate (FPR) of 25%. In this white paper, you will learn how advanced technology can reducethe FPR to 5%. The paper also details a two-step process for capturing images as well as an algorithm to perform a binary classification for model development and testing.
Current computer vision-based methods for identifying broken teeth on mining shovels suffer from a prohibitively high false positive rate (FPR) of 25%. We describe a 2-stage methodology for the detection of broken teeth that reduces the FPR to 5%. First, we used a Haar wavelet feature cascade based on the Viola-Jones object detection framework to detect the row of shovel teeth from the input image. The second stage is a classification step that takes the detections from stage 1 as input and produces a binary score indicating whether the equipment is intact or damaged. We evaluated two methods for stage 2: 1) Dynamic Time Warping with k-Nearest Neighbors (DTW—k-NN) and 2) Convolutional Neural Network (CNN). The accuracies of the two methods on an out-of-sample image set were 96.3 and 95.5%, respectively.
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