# Effective Fabric Defect Detection Model for High-Resolution Images
## 1 Introduction  
The evolution of digital technologies is placing companies in front of a potential paradigm shift, characterized by greater interconnection and cooperation between systems, people, and information. This technological mix of automation, information, connection, and programming is leading to the birth of the fourth industrial revolution, also known as Industry 4.0. In the field of smart manufacturing, one of the most important sectors is manufacturing inspection, which consists of detecting product defects. Current inspection systems cannot guarantee good performances while keeping processing efficiency and reducing inter-subject variability. Therefore, quality control procedures with both performance and efficiency are essential [1]. Factories produce a large amount of fabric with defects every year, which warrants the development of fabric defect detection methods to ensure its quality. Currently, most factories employ workers to inspect fabric defects, but they are restricted by limitations such as low accuracy and easy fatigue [2]. Hence, a high-precision automatic fabric defect detection system is urgently required. With the development of computer technologies, many scholars have attempted to use image processing algorithms, such as traditional image processing technology and object detection algorithms based on deep learning to automatically identify fabric defects [3]. Traditional image processing technology is used to detect defects with simple fabric pattern backgrounds; it cannot extract features with complex backgrounds. In the context of Industry 4.0, the increasing availability of data, the advancements in computing power, and breakthroughs in algorithm development haveled machine learning (ML) and deep learning (DL) methodologies to develop appealing solutions in different industrial areas such as predictive maintenance, decision support system (DSS), and quality control (QC) [4]. The object detection algorithm based on deep learning has many advantages, such as high robustness and detection accuracy. Consequently, deep learning algorithms have become the preferred choice for fabric defect detection [5; 6]. Object detection algorithms based on deep learning can be developed quickly [7]; therefore, several researchers have attempted to use them to automatically detect fabric defects to increase production profit. For example, [PERSON] et al. proposed a fabric defect detection algorithm based on Faster R-CNN [8]; they mainly used the deformable convolution network and distance IoU loss function to enhance the detection results, but this method is strict in the selection of threshold value and has poor robustness. Based on convolutional neural networks, [PERSON] et al. proposed a new network for detecting fabric defects [9]; furthermore, they proposed a method to generate priori anchors to locate objects more precisely, this method has achieved good results in fabric images with simple backgrounds, but poor results in fabric images with complex backgrounds. Based on YOLOv3, [PERSON] et al. proposed a method to select the number and size of prior frames, followed by adjusting the YOLO detection layer to decrease the error detection rate [10]. The detection speed of this method is fast, but there is still a lot of room for improvement in detection precision. It should be noted that research on the detection of fabric defects has progressed significantly, and the detection of fabric images with small sizes and simple backgrounds has basically reached the industrial demand. Nevertheless, it is difficult to obtain high detection precision on fabric images with a large size and complex background, which is the main focus of this study. In our work [11], classical object detection models such as SSD [12], YOLO [13; 14; 15], Faster CNN [16; 17; 18], and Cascade R-CNN [19] were trained and tested. The Cascade R-CNN model was eventually selected as the base network; accordingly, some optimizing strategies were introduced to improve its detection accuracy.