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The occurrence of faults in textile manufacturing methods results in major wastage of the properties. Additionally, it also affects the quality of the fabric products. Manual inspection methods fail to detect the defects with respect to efficiency, accuracy, and consistency due to size of the detects, carelessness, and optical illusion. Therefore, automatic fabric inspection has become a powerful tool to optimize the quality of fabric. In this background, the current study develops a novel Hybrid Mutation Moth Flame Optimization with Deep Learning-Based Smart Fabric Defect Detection (HMFODL-FDD) technique for sustainable manufacturing. The proposed HMFODL-FDD technique exploits the Computer Vision (CV) and Deep Learning (DL) techniques for the detection of defects in fabric. To accomplish this objective, the presented HMFODL-FDD technique employs contrast enhancement process to boost the quality of the …