Deep Learning-Driven Automated Inspection for Defect Detection in Leather and Footwear Manufacturing: A Comprehensive Review
الموضوعات : Artificial Intelligence
1 - Department of Industrial Engineering College of Engineering, Chennai – 600025
الکلمات المفتاحية: Deep learning, automated inspection, leather defect detection, footwear quality control, computer vision, Industry 4.0, species identification, color analysis, AI in manufacturing,
ملخص المقالة :
Automated inspection systems powered by deep learning are revolutionizing quality control in leather and footwear manufacturing by replacing subjective, time-consuming manual methods with objective, high-throughput solutions. This review presents a comprehensive analysis of deep learning-driven inspection approaches for defect detection in leather and footwear, covering both conventional image processing techniques and state-of-the-art architectures such as convolutional neural networks (CNNs), YOLO series models, and vision transformers. Key application areas include color prediction and sorting, leather species identification, and defect segmentation/classification, with emphasis on integration into real-time industrial workflows. The study examines how the adoption of AI-based inspection improves product quality compliance, reduces rejection rates, and enhances manufacturing competitiveness. It also highlights the transition toward Industry 4.0-aligned inspection systems and identifies current challenges such as dataset scarcity, small-defect detection limitations, and integration with high-speed production lines. Finally, the review proposes future research directions for developing adaptive, domain-specific deep learning models that support scalable, reliable, and sustainable leather and footwear production.
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