Fault Detection Using Canny Edge Detection and Mask R-CNN in Injection Molding of Manufacturing Processes

Abstract

In various injection molding manufacturing plants, there are many difficulties in detecting defective products during production. Since there are limitations in detecting product defects with the human eye, this paper proposes a framework for detecting product defects in a human-free manufacturing environment. We detect product defects using Canny Edge Detection, a powerful edge detector, and provide reliability of products detected using Mask R-CNN, a neural network with excellent speed and accuracy. As the network, the ResNet101 network with the highest accuracy was selected, and the network was used as the backbone network of Mask R-CNN, and the image was resized and sized using LEDs when shooting to detect even small scratches.