基于ConvNeXtBase和Coyote优化额外树的可靠视觉智能皮革缺陷检测模型

IF 3.9 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Pattern Recognition Letters Pub Date : 2025-07-07 DOI:10.1016/j.patrec.2025.06.019

Brij B. Gupta , Akshat Gaurav , Razaz Waheeb Attar , Varsha Arya , Ahmed Alhomoud

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引用次数: 0

摘要

皮革行业不断努力确保高产品质量，但在鞣制，染色和材料处理等阶段经常出现缺陷。传统的人工检查是不一致的，这就产生了对自动化、可靠的视觉智能系统的需求。介绍了一种基于ConvNeXtBase和Coyote优化额外树的可信视觉智能皮革缺陷检测模型。使用ConvNeXtBase进行特征提取，使用COA优化的ExtraTreesClassifier进行准确的缺陷分类，识别出脱粒、松粒、针孔等问题。与SVM、XGBoost和LGBMClassifier等模型的比较分析表明，该方法具有更高的准确率（0.90）、精密度、召回率和F1分数。coa优化的ExtraTreesClassifier是高效的，使其成为实时工业应用的理想选择。

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Trusty Visual Intelligence Model for Leather Defect Detection Using ConvNeXtBase and Coyote Optimized Extra Tree

The leather industry continuously strives to ensure high product quality, yet defects often arise during stages like tanning, dyeing, and material handling. Traditional manual inspections are inconsistent, creating a need for automated, reliable visual intelligence systems. This paper introduces a Trusty Visual Intelligence Model for Leather Defect Detection Using ConvNeXtBase and Coyote Optimized Extra Tree. ConvNeXtBase is utilized for feature extraction, while an ExtraTreesClassifier, optimized with the Coyote Optimization Algorithm (COA), is employed for accurate defect classification, identifying issues like grain off, loose grains, and pinholes. Comparative analysis with models such as SVM, XGBoost, and LGBMClassifier demonstrates superior accuracy (0.90), precision, recall, and F1 score. The COA-optimized ExtraTreesClassifier is efficient and effective, making it ideal for real-time industrial applications.

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来源期刊

Pattern Recognition Letters 工程技术-计算机：人工智能

CiteScore

12.40

自引率

5.90%

发文量

287

审稿时长

9.1 months

期刊介绍： Pattern Recognition Letters aims at rapid publication of concise articles of a broad interest in pattern recognition. Subject areas include all the current fields of interest represented by the Technical Committees of the International Association of Pattern Recognition, and other developing themes involving learning and recognition.