Efficient Neural Network Approaches for Leather Defect Classification

Abstract

Genuine leather, such as the hides of cows, crocodiles, lizards and goats usually contain natural and artificial defects, like holes, fly bites, tick marks, veining, cuts, wrinkles and others. A traditional solution to identify the defects is by manual defect inspection, which involves skilled experts. It is time consuming and may incur a high error rate and results in low productivity. This paper presents a series of automatic image processing processes to perform the classification of leather defects by adopting deep learning approaches. Particularly, the leather images are first partitioned into small patches, then it undergoes a pre-processing technique, namely the Canny edge detection to enhance defect visualization. Next, artificial neural network (ANN) and convolutional neural network (CNN) are employed to extract the rich image features. The best classification result achieved is 80.3%, evaluated on a dataset that consists of ∼ 2000 samples. In addition, the performance metrics such as confusion matrix and Receiver Operating Characteristic (ROC) are reported to demonstrate the efficiency of the method proposed.

Experimental Study on YOLO-Based Leather Surface Defect Detection

“…Aslam [23] believes that deep learning holds great promise in developing new solutions for leather surface defect inspection. Some researchers have also developed corresponding solutions [24][25][26][27]. Although Liong's team conducted an in-depth exploration, their work was also limited to a small local dataset.…”