An approach to automatic fault detection in four-point system for knitted fabric with our benchmark dataset Isl-Knit

Abstract

Knit fabric is one of the dominating fabric types for wearable textile across the whole world and during the production of knit fabric, faults created by different reasons cause difficulties in the subsequent process. The existing fault detection process in the knitting industry is done manually by the human naked eye. To detect faults automatically, deep learning-based models are very efficient and can reduce the workload for fabric inspection. However, implementing a deep learning-based model for fabric fault detection needs a large number of images with different types of knit fabric faults from real-world scenarios to train and for better performance. Most of the existing fabric fault datasets are based on woven fabric, which cannot be used for training because the faults of woven fabric and knit fabrics are completely different. In this research, we propose a new dataset named ISL-Knit. It is a benchmark dataset for knit fabric faults collected from different knit dyeing industries in Bangladesh. Our dataset contains high-resolution images of grey fabric and dyed fabric annotated with 7 types of faults. To provide correct annotation, all images of faults are annotated and verified with the proper reference which can be a great tool for boosting up deep learning-based models for automatic knit fabric fault detection in the textile field. A comparison of the accuracy of the deep learning-based model by training with our proposed dataset and existing knit fabric fault dataset is also done. To develop an automatic fault detection system, we trained the YOLOv5 models with our dataset, considering different image sizes. For real-world implementation, the best models are selected considering mAP and inference time. YOLOv5 models are implemented in Raspberry Pi with a Raspberry Pi camera, a laptop with no dedicated GPU, and a laptop with a dedicated GPU with a web camera to observe the performance. Finally, an automated four-point inspection system is developed by calibrating the camera to generate the score representing the quality of fabrics. The implementation represents the feasibility of a deep learning-based model in knit fabric fault detection, considering the cost and accuracy.