## 2 Limitations of Existing Object Detection Models  
Although deep learning-based object detection models have made remarkable advances, their low detection rate for small objects remains a problem to be solved. The simplest solution for this problem is to reduce the loss rate for small objects by increasing the resolution of the input image [12]. Another solution is to combine low- and high-level feature maps to create multi-scale representations that enhance the features. Another approach is to perform prediction at different layers; this results in feature maps of various scales [13]. Nonetheless, the difficulty in detecting small objects remains a problem. The following studies were conducted to resolve this problem [14,15]. [PERSON] et al. used a Gaussian filtering technique, which is an image processing technology, and the Otsu algorithm, which is an adaptive binarization technique, to detect pit defects in steel shells [16,17]. They proposed an algorithm that can detect positive regions, where steel shells are present and applied Gaussian filtering and binarization to the interior of the ellipse to detect the existence of pits. To detect five types of defects that occur in the process of manufacturing, [PERSON] et al. proposed the TICNET model, which improves the existing cascade R-CNN approach [18]. The feature extraction capacity of the model was improved by applying deformable convolution layers instead of convolution layers; this can learn both weights and kernel offsets [19,20]. In addition, to concentrate the model's learning on objects that are difficult to classify among the defect samples, they enhanced the features of cascade-RCNN and improved the learning performance for complex defect patterns. [PERSON] et al. attempted to apply parameter optimization on fast R-CNN to detect four types of defects, including pits, present on the surface of a shaft [21]. Because it is difficult to find small defects with a minimum size of 0.3 mm in images with a size of 16,384 \(\times\) 4096 pixels obtained through line scanning, the images were manually split into images of size 500 \(\times\) 500 pixels. It was observed that for a single IoU value, the performance decreased when the ratio of negative samples was increased; therefore, they proposed a method of setting multiple IoU values to increase the ratio of positive samples. [PERSON] et al. proposed a neural network that sets anchor boxes optimized for small object sizes; it uses a multi-scale representation learning method that combines high- and low-level feature maps to create feature maps that contain more feature information regarding small objects [22]. In addition, they added contextual information about the areas around small objects and used this information in feature maps to resolve the imbalance problem of insufficient small object training data. Studies have also been conducted on methods for improving the detection performance using data augmentation techniques that have properties suitable for small object detection [23,24]. Table 1 shows the input image resolutions and resulting detection times of previous defect detection studies. When the detection targets are small objects such as pit defects, the deep learning and machine learning methods all take more than 2 s on average. By contrast, in the case of larger ordinary objects, these methods are capable of processing even high-resolution images within approximately 120 ms because such objects can be adequately found using a lightweight architecture. Therefore, finding small objects during tasks requiring real-time processing is a problem of considerable difficulty. In previous studies, deep-learning-based methods have the disadvantage of having to manually divide the input image to avoid a loss of the small object area during the forward propagation. Besides, previous image-processing-based methods require complex feature extracting operations to detect small defects accurately, which results in a problem of decreasing processing speed.