The vast majority of goat skin processed by traditional tanneries comes from small rural producers. Thus, with the predominance of rustic creation, slaughter, and skinning methods, the batches of hides processed by tanneries have a very heterogeneous quality. Thus, there is a need to categorize the samples according to the quantity and location of defects. The categorization process is subjective and strongly influenced by the experience of the professional classifier, causing a lack of homogeneity in the composition of the goat hide lots for sale. Aiming to reduce failures in the categorization of goatskin samples, the authors investigate the application of computer vision and artificial intelligence on a set of previously categorized wet blue goatskin photographic samples. That said, is analyzed the capacity of different classifiers, with different paradigms, in detecting defects in goatskin samples and in categorizing these samples among seven possible quality levels. A hit rate of 95.9% was achieved in detecting defects and 93.3% in categorizing quality levels. The results suggest that the proposed methodology can be used as a decision aid tool in the qualification process of goat leather samples, which can reduce sample labeling errors.

传统制革厂加工的绝大多数山羊皮来自农村小生产商。因此，由于以质朴的创造、屠宰和剥皮方法为主导，制革厂加工的各批生皮质量参差不齐。因此，需要根据缺陷的数量和位置对样品进行分类。分类过程是主观的，受专业分类人员的经验影响很大，导致待售山羊皮组的组成缺乏同质性。为了减少山羊皮样本分类的失败，作者研究了计算机视觉和人工智能在一组先前分类的蓝湿山羊皮摄影样本上的应用。也就是说，分析了不同分类器的容量，使用不同的范例，检测山羊皮样品中的缺陷并将这些样品分为七个可能的质量等级。检测缺陷的命中率达到 95.9%，质量等级分类的命中率达到 93.3%。结果表明，所提出的方法可以用作山羊皮革样品鉴定过程中的决策辅助工具，可以减少样品标记错误。