Deep Learning and Image Processing for Disc Brake Pad Identification: A Case Study of Brake Pads Company
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Abstract
Disc brake pad identification is a difficult task that requires experiences of disc brake experts. However, disc brake pad retails must identify the part to deliver the right product to customers. In this research, the deep learning algorithms and object detection technologies to help identify disc brake pad in a case study of disc brake pads company are proposed. The goal is to implement disc brake identification system that finds the right brake pad model correctly in an instant time. We select two deep learning algorithms that are well known in object detection which are YOLOv5 and Faster R-CNN. The disc brake pad detection performance of the two algorithms is compared. There are four measurements: precision, detection speed, loss function (regression loss, classification loss), and training time. We use the two algorithms to detect and classify five disc brake pad models. The results show that YOLOv5 has better precision, detection speed, loss function, but Faster R-CNN requires less training time.
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