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@article{203027,
        author = {Akash H and Hemanth Kumar},
        title = {FABRIC STAIN DETECTION USING YOLOv8},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {9589-9595},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203027},
        abstract = {Textile industry is only few of the oldest industries in India and also in the world. For many years, fabric checking was mainly done by human workers. The workers inspect the fabric through visual examination to detect any defects or stains. The manual method of inspection exhibits multiple operational difficulties. Workers experience fatigue during extended inspection periods which results in their ability to overlook minor stains. The process requires an extensive amount of time to review the entire fabric inventory. The method of manual inspection produces inconsistent results because various inspectors make different evaluations. The process of fabric inspection becomes challenging because a person needs to examine thousands of meters without making errors. The textile factories encounter difficulties with quality control because of this problem. Our project aims to create an automatic system that detects fabric stains through computer vision (CV) and learning of deep technology.The research employs YOLOv8 which functions as a high-speed object detection system. The system analyzes video content through its frame-by-frame processing capability to identify High Stain and Low Stain patterns on fabric surfaces.},
        keywords = {Fabric Defect Detection, Yolov8, Deep Learning, Stain Detection, Textile Inspection, Convolutional Neural Network, Automated Grading System},
        month = {May},
        }