## 2 Related Works
Traditional manual inspection methods are time-consuming and labor-intensive, while machine vision systems operate at high speeds, enabling real-time inspection of parts on the production line. This not only accelerates manufacturing processes but also facilitates early defect detection, preventing costly rework and downtime, and ultimately improving productivity. Machine vision systems generate vast amounts of data on product quality, process parameters, and performance indicators. Manufacturers can apply advanced analytics and machine learning algorithms to derive actionable insights from these data, optimize processes, predict maintenance requirements, and drive continuous improvement initiatives [20; 21; 22]. As the cornerstone of smart manufacturing, machine vision enables manufacturers to achieve unprecedented levels of precision, efficiency, and quality during production. By utilizing advanced imaging technology, analysis, and automation, machine vision surpasses the limitations of traditional inspection methods and ensures high-quality manufacturing. AI-based inspection and analysis systems, which integrate artificial intelligence and machine learning algorithms, facilitate even more complex inspection tasks, adapt to evolving production environments, and offer deeper insights through advanced analytics. Research on defect detection using machine vision based on deep learning algorithms has revolutionized quality control across various industries. For example, a study on classifying defects on flat steel surfaces highlighted defect classification after detection as an essential step in online quality inspection, and various methodologies have been investigated [1]. In agriculture, convolutional neural networks (CNNs) have been used to detect surface defects on fruits like mangoes, achieving a high accuracy of 98% [2]. Additionally, techniques such as YOLO v2 have been applied to detect railway surface defects quickly and accurately, achieving a high accuracy of 97.11% [15; 23]. The advancement of deep learning has significantly improved the performance of various defect detection algorithms, with studies centered on CNNs demonstrating high accuracy and real-time processing capabilities [3; 6]. However, these systems still rely on large, high-quality datasets, and data augmentation methods using Generative Adversarial Networks (GANs) are being further researched to address data imbalance issues. These technological developments have contributed to increased manufacturing efficiency and reduced the burden on human resources. Machine vision systems, although expensive, can create models for easy training and inspection if provided with images of both good and defective products [24; 25; 26; 27]. When defective samples are scarce, performance can be enhanced by generating a large volume of training images using in-house developed image augmentation functions and generative algorithms for defect images. Large companies generally have the resources to mass-produce images of both good and defective products for training and many have already implemented high-precision, high-speed machine vision systems and smart manufacturing processes with such advanced equipment. However, small and medium-sized enterprises (SMEs) often lack established smart manufacturing processes and face challenges in acquiring costly machine vision equipment. Additionally, training deep learning models typically requires a substantial quantity of images of both good and defective products. Obtaining defective samples can be difficult, depending on product characteristics and the manufacturing environment. In some cases, it may not be feasible to capture images of multiple defect types on a single product or to configure a single lighting setup capable of detecting all defect types in one image. Given these field challenges, the optimal approach is to develop a customized hardware-software integrated system capable of high-speed real-time defect detection, specifically tailored to each manufacturer, with user- and process-specific optimizations.