## 2 Related Works  
Deep learning technology has been reported to outperform conventional rule-based visual inspection systems in detecting various types of assembly defects and conductingquality inspections in complex industrial settings [4; 5; 6]. However, high-quality training images are required to improve the performance of deep learning-based inspection systems. Most studies conducted on deep learning technology have been implemented on small-scale inspection targets with limited variation under controlled lighting and environmental conditions. In several industrial settings, it is difficult to obtain high-quality training images due to uncontrolled lighting and environmental conditions, along with frequent changes in the inspection targets, making it difficult to acquire sufficient training data. Previous studies have attempted to implement image acquisition and deep learning inspection under conditions similar to those of actual industrial sites. In [PERSON]'s study, assembly workers directly used wearable lenses and headsets to acquire images. The time required to capture the part images was set by using the worker's position and gaze information [7]. The evaluation results demonstrated that the system accuracy was low, at 85%. This was because even in a laboratory environment where the lighting conditions were consistent, there were image variations based on the distance and angle at which the worker captured the image. In [PERSON]'s study, deep learning technology was implemented to inspect the surface treatment quality of automobile assembly parts. After sufficient training data were obtained, deep learning technology showed superior inspection performance when compared to the existing rule-based visual inspection method [3]. However, the inspection algorithm was only limited to the surface inspection of brake pedal parts that did not change when a new vehicle was released, and it was developed only after obtaining sufficient training data. Research is being conducted to address the challenge of obtaining sufficient data in industrial settings. The data acquired in actual industrial settings are small in quantity, but there is a significant difference in the OK and NG ratios. In the case of manual assembly processes for automobiles, data acquisition can be performed with an OK rate of 99% and an NG rate of 1%. Therefore, NG items must be created arbitrarily to acquire sufficient training data within a short period. This involves substantial time and cost. The one-class neural-network method, a type of semi-supervised learning, has been proposed to address this issue. This method learns using only a small number of normal images and detects samples that differ from the normal samples as outliers [8]. However, uncontrollable environments such as lighting can cause severe image variations due to image exposure. In the case of automobile parts comprising flexible cables and connectors, the position of the cable varies even in normal images, along with the position of the surrounding parts. Therefore, it is difficult to determine the boundary between bad and normal images. Even if an appropriate boundary is set, it frequently changes due to the rapidly changing environmental conditions, resulting in decreased inspection accuracy and increased maintenance costs. Therefore, a system must be developed that can acquire and inspect images under conditions where it is difficult to secure learning data of sufficient quality due to frequent changes in the inspection parts at industrial sites. Additionally, the high time and cost requirements for repeatedly maintaining detection algorithms due to frequent changes in the inspection items make it difficult to implement deep learning technology. In actual industrial sites, developing and implementing a deep learning inspection system can be challenging owing to the high cost required for the development and maintenance of the inspection system.