Defect Detection and Quality Level Assignment in Wet Blue Goatskin

Tanneries acquire hides, in most cases, from rural workers, so, due to the informality of the creation, slaughter and extraction of the animal's skin, they receive them with different types and different defect levels. That said, classifying acquired and processed skins become very complex and tiring activity. The leather discrimination process is completely handmade and subjective, too dependent on the experience of the professional responsible for this step, which, due to tiredness, stress, and other factors, end up generating several errors in this process. Currently, there are several studies in the literature related to the detection of leather flaws, however, few studies go further and qualify the skins based on the detected problems. In view of this factor, a system based on computer vision and artificial intelligence are proposed in which it obtains an accuracy rate of 95.9 % in the detection of defects in wet blue goatskin and 93.3 % in the identification of the quality level of these parts.