  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 data set 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.
