## 1 Introduction  
Automobiles, trains, and large electronic products are made by combining various parts. These combinations, in turn, require the use of techniques such as welding, assembly, and bolt fastening. Among them, bolt fastening is still widely used to combine parts because it allows different parts to be connected without special technology. Bolts, however, may become unfastened owing to vibration or shock, which can be hazardous where safety-critical parts are combined using the bolt fastening technique. To resolve this problem, in products such as Nylock, a liquid nylon coating is applied between the bolt threads, thereby increasing the frictional graft force and the fastening power. These products are called anti-loosening coated bolts (Figure 1). Anti-loosening coated bolts are manufactured by automatically or manually rotating the coating liquid to spread it between the bolt threads. During this process, the bolts may become defective owing to errors in the liquid viscosity or the quantity of applied liquid. As quality is critical in stabilizing the fastening force, the dried bolts go through a sorting process, during which defective and non-defective bolts are separated. The sorting process is conducted by automated optical sorters. Although four types of defects can occur in coated bolts, the optical sorters are not capable of identifying all of them because they are reliant on colors to find the defects. Therefore, human vision is used to provide an additional sorting process. However, manual sorting activities with the naked eye are repetitive and, therefore, place high stress on the human workers, ultimately reducing the accuracy of the process. Furthermore, rising labor costs and limited sorting speeds negatively impact the production volume. To improve upon the shortcomings of the optical and naked eye sorting methods, various industries have been implementing artificial intelligence technology in the sorting process. [PERSON] et al. [1] utilized the support vector machine method, a machine learning technique, to detect surface defects with 85% accuracy. [PERSON] and [PERSON] [2] applied machine learning technology to detect compact camera module (CCM) defects. Deep learning technology is frequently used to generate algorithms for defect detection because the image-learning characteristics and defective product sorting technology performs well [3; 4; 5; 6]. Deep learning has been used to detect defects in railway track fasteners [7] and bottle surfaces [8]. It has also been shown that even the smallest defects in components can be detected using deep learning techniques [9]. [PERSON] and [PERSON] [10] used deep learning techniques to identify cracks, scratches, and other irregular flaws often observed in industrial parts. To identify irregularly shaped defects arising from processes such as welding, researchers used machine learning and image filtering technology [11]. Image-based deep learning techniques, however, result in a loss of pixel location data due to the inherent flaws in the convolutional neural network (CNN) structure, as well as flaws in the technology in identifying characteristics and patterns from the learned data to independently classify the results. Additionally, these techniques require a high volume of data to determine specific features, which may be difficult to obtain for high-cost products that have insufficient data or for products with a significantly low defect rate. For the anti-loosening coated bolts in the current study, the problem of insufficient samples found in the production process was experienced. To verify the reliability of the CNN-based coated bolt sorting model and resolve potential issues, in this study, we applied visualization and transfer learning techniques to sort anti-loosening coated bolts and automate the sorting process. First, defective anti-loosening coated bolts were sorted according to established standards.