Deep Residual Neural Network-Based Defect Detection on Complex Backgrounds

Abstract

An inspection device with a set of lights, a six-axis robot arm, and a camera is designed for image acquisition. A deep residual neural network (DRNN) performs both the feature extraction and classification tasks simultaneously. This article makes several modifications of ResnNet50 to ensure accurate and reliable predictions. The dataset consists of a collection of images of a plastic casing with different types of scratches. Most defects in the dataset are thin, shallow, and small. Due to the spatial reduction, defects fade because of their features while performing the deep residual network. Thus, mask labeling based on pixel annotation crops a subimage using a sliding window. Efforts have been made to solve the faded issue while DRNN. The experimental results for 600 plastic casing images show that the proposed method significantly increases the convolutional algorithm’s ability to detect defects accurately. The defect detection accuracy is approximately 96.38%.