# A Study on the Optimization of the Coil Defect Detection Model Based on Deep Learning
## 1 Introduction  
A quality inspection system is used for obtaining consistent quality results at manufacturing sites. Quality inspection systems afford advantages such as product quality improvements and solutions to labor shortages to companies. However, existing quality inspection systems may differ in inspection performance because of the fatigue and different skill levels of human inspectors due to a rule- or visual-based inspection. In the present study, one surface of a coil--an inspection target--is inspected as shown in Figure 1A. Approximately 1-1.5 s are required for an inspector to inspect around 10 coils (Figure 1B). According to Figure 1B, the inspector inspects only one side during the inspection time. In addition, Taiwan's inspectors must take a certain amount of time off during working hours.  
To consistently maintain the performance of quality inspection systems and reduce human errors, deep-learning-based machine vision technology has been applied in quality inspection, and related studies have been actively conducted. With the Fourth Industrial Revolution, considerable attention has been paid to artificial intelligence (AI) and recognition technologies based on AI have greatly advanced. As significant attention has been paid to AI technology even at manufacturing sites, several studies on the application of intelligent machine vision technologies to quality inspection systems have been conducted [1, 2].  
Machine vision technology using deep learning shows a performance of about 95% when evaluated by mAP, and is used in various fields [3, 4, 5]. The machine vision market is also projected to witness tremendous growth in the future. As depicted in Figure 2A, the global machine vision market will grow at approximately 7% annually, resulting in a market of around USD 15.5 billion by 2026. In particular, hardware and software marketsin machine vision will grow at approximately 6% and 10.3%, which will be worth around USD 11.3 billion and 4 billion, respectively, as shown in Figure 2B.  
In this study, a quality inspection system based on deep learning technology is proposed. YOLOv5 is a model of the YOLO family, a real-time object detection model, that can find desired objects in a short time [7]. In this study, model optimization for defect detection of small objects is carried out using the YOLO family of models. Various model performance improvement techniques are used to analyze the impact on model performance, and model optimization is carried out based on the results. In addition, additional model optimization is carried out by adding data that has undergone image preprocessing to effectively extract the features of the image.  
Related works associated with this can be found in Section 2. In Section 3, the proposed quality inspection system is explained and a model using the system and various performance improvement techniques for the model (algorithm structure optimization, model scaling, and light weight) are analyzed. In Section 4, the results of the performance  
Figure 1: Example of coil—visual inspection.  
Figure 2: Forecast of the global machine vision market [6].  
improvement techniques, which are applied to the model, are comparatively analyzed to propose the optimum model of the intelligent machine vision inspection system.