Deer Hunting Optimization with Deep Learning-Driven Automated Fabric Defect Detection and Classification

The detection of fabric defects (FD) has become crucial in the fabric industry; however, it has some limitations due to the complex shapes and various types of FDs. The standard approach is to identify defects using human vision that can support workers to repair minor defects directly. However, the performance of manual detection is decreased slowly with increases in working hours. Consequently, there is a need to develop an automatic inspection system for FDs to improve fabric quality and decrease human work costs and errors. Recently, deep learning (DL) and computer vision (CV) approaches become popular for automated classification and recognition of FDs. Therefore, this study presents a Deer Hunting Optimization with a Deep Learning-Driven Automated Fabric Defect Detection and Classification (DHODL-AFDDC) method. The study aims to design and develop a hyperparameter-tuned DL approach for the automated recognition and classification of FDs. To achieve this, the DHODL-AFDDC method exploits an augmented MobileNetv3 approach for the feature extraction process. Furthermore, the efficiency of the improved MobileNetv3 approach can be boosted by the utilization of a DHO-based hyperparameter tuning process. Moreover, the recognition and classification of FDs take place by employing the bidirectional long-short-term memory (BiLSTM) technique. An extensive set of experiments were conducted to demonstrate the enhanced outcome of the DHODL-AFDDC technique. The stimulation outcomes highlighted the improved performance of the DHODL-AFDDC method compared to recent approaches.