# A Study on the Optimization of the Coil Defect Detection Model Based on Deep Learning
## 2 Related Research  
Studies have been conducted on methods to improve model performance, such as modifying a model structure or combining a model with other models to reduce computation and improve inference speed. Using the YOLO family model, we analyzed performance improvement studies with model and dataset modifications.  
In order to improve model performance, the main method is to modify the existing model using other models. This can be confirmed through the following studies. [PERSON] et al. [8] used the EfficientNet model to classify defects in advance, and based on this result the YOLOv5 model was used to indicate the location of defects. [PERSON] et al. [9] used two YOLO algorithm versions to detect microscopic images of defects that were discovered in the process and conducted a comparative analysis of their performance. In their study, YOLOv3 and YOLOv5s models were used. They found that the YOLOv5s model exhibited better inference speed with a relatively small weight capacity than that of the YOLOv3 model, and the mean average precision was around 1% higher as well. [PERSON] et al. [10] applied the GhostBottlenet module instead of using the backbone's Conv module in the existing YOLOv5 model to reduce the network computation by compressing the parameters in the YOLOv5 model and increase the model's inference speed. The GhostBottlenet module reduced the computation by approximately 36% compared with that of the YOLOv5s model and improved performance by approximately 2%. [PERSON] et al. [11] applied a deep-learning-based machine vision inspection system to detect surface defects in steel. Here, YOLOv5 and U-Net, representative computer vision algorithms, were applied to the study. The applicability of a deep-learning-based machine vision inspection system was demonstrated by showing high-level defect detection results.  
In addition, methods are being studied to improve model performance by solving problems arising from unbalanced or small amounts of data. To resolve these problems, a study using data augmentation, feature extraction, and noise removal was conducted as follows: [PERSON] et al. [12] augmented an amount of data using an autoencoder to solve the data imbalance that occurred in defect detection and verified the performance improvement through experiments. In addition, [PERSON] et al. [13] also described the performance differences according to various versions of YOLOv5 and data augmentation. The larger the size of the model and the more datasets used for learning, the longer the learning time was, but the performance tended to improve. The unique characteristics vary according to the inspection target. Thus, techniques that can well extract the characteristics of the target should be applied. [PERSON] et al. [14] aimed to easily extract defects of the inspection target using the region of interest and Hough transform techniques.  
Analyzing the above research trends, there are various methods of using the model according to the purpose and direction of the study. This process depends on the purpose and direction of each study, and the performance of the model also varies. Therefore, it is important to implement an appropriate model by utilizing high-quality datasets. Based on the preceding studies, the present study attempted to improve the model performance from the data and the model viewpoint to enhance the performance of the existing YOLOv5 model. To improve the model performance, image data upgraded through the histogram equalization of datasets and performance-enhancement techniques such as structure modification, model scaling, and a lightweight model design are used. Unlike previous studies, this study does not present only differences from existing models. Various techniques for improving performance are applied and the results are analyzed. Trends are analyzed through result analysis, and the impact on performance is analyzed. Based on this, we present a model optimized for small object detection.