Vision based leather defect detection: a survey

Increasing consumer quality awareness and increase in consumer wealth drives the market demand for high quality leather and leather products. Reliable and effective detection and classification of leather surface defects is of profound significance to tanneries and industries where leather is a major raw material for leather accessories and leather parts manufacturers. This paper presents a methodical and a detailed review of the leather surface defects detection methods starting from leather image acquisition, leather image processing, feature extraction and classification for defect detection. Firstly, we introduce the fundamentals of leather image acquisition and various related image processing methods, feature extraction and classification for the defect inspection. Next, the existing datasets and summary of the recent methodologies used in this field are discussed. Finally, the challenges and suggested improvements to further the development of the application of advanced Machine Learning and Deep Learning in this field are discussed. Deep learning algorithms are shown to have a great potential for leather surface defect detection and can help prepare a robust system that would greatly guarantee quality leather and provide monetary wealth from such leather products. Finally, research guidelines are presented to fellow researchers regarding data augmentation, leather defect detection models which need to be investigated in the future to make progress in this crucial area of research.