Varghese Anjli, Jawahar Malathy, Prince A Amalin
Department of Electrical and Electronics Engineering, BITS Pilani, K K Birla Goa Campus, Goa, 403726, India.
Central Leather Research Institute, Chennai, 600020, India.
Sci Rep. 2025 Feb 6;15(1):4493. doi: 10.1038/s41598-025-88040-1.
In the leather industry, the mammalian skins of buffalo, cow, goat, and sheep are the permissible materials for leather-making. They serve the trade of quality leather products; hence, the knowledge of animal species in leather is inevitable. The traditional identification techniques are prone to ambiguous predictions due to insufficient reference studies. Indeed, leather image analysis with big data can pave the way for automatic and objective analysis with accurate prediction. This study focuses on creating novel and unique leather image data, LeaData. The objective is to automatically determine species from grain surface analysis. Hence, it employs a simple, cheaper, handheld digital microscope for leather image acquisition. The magnifying parameter 47× captures the species-unique grain patterns distributed over the leather surface. In total, the LeaData encloses 38,172 images of four species from 137 leather samples. This big data spans leather images with theoretically ideal and practically non-ideal grain patterns. It also includes images of grain patterns varying over different body parts. Thus, the novel LeaData is an adequately larger pool of leather images with diverse behavior. The motive is to establish a smart leather species identification technique that can be easily accessible by leather specialists, customs officials, and leather product manufacturers. Hence, this paper solely creates the bigger LeaData and presents its different versions to the digital image processing and computer vision research community. This digitized source of permissible leather species helps enable digitization in leather technology for species identification. In turn, in maintaining biodiversity preservation and consumer protection.
在皮革行业，水牛、奶牛、山羊和绵羊的哺乳动物皮是制作皮革的许可材料。它们服务于优质皮革产品贸易；因此，了解皮革中的动物种类是必不可少的。由于参考研究不足，传统的识别技术容易出现模糊的预测。事实上，利用大数据进行皮革图像分析可以为自动、客观且预测准确的分析铺平道路。本研究专注于创建新颖独特的皮革图像数据LeaData。其目标是通过纹理表面分析自动确定物种。因此，它采用了一种简单、便宜的手持式数字显微镜来采集皮革图像。47倍的放大参数能够捕捉分布在皮革表面的物种独特纹理图案。LeaData总共包含来自137个皮革样本的四种物种的38172张图像。这些大数据涵盖了理论上理想和实际中不理想纹理图案的皮革图像。它还包括不同身体部位纹理图案各异的图像。因此，新颖的LeaData是一个具有多样特征的足够大的皮革图像库。目的是建立一种智能皮革物种识别技术，皮革专家、海关官员和皮革产品制造商都可以轻松使用。因此，本文仅创建了更大的LeaData，并将其不同版本呈现给数字图像处理和计算机视觉研究社区。这种数字化的许可皮革物种来源有助于实现皮革技术中用于物种识别的数字化。进而有助于维护生物多样性保护和消费者保护。
