# Dual-Kernel-Based Aggregated Residual Network for Surface Defect Inspection in Injection
## Literature Review  
### Surface Defect Detection  
Several models of defect detection for industrial products have been developed since the convolutional neural network (CNN) was developed by [PERSON] et al. [11]. The CNN model has led to significant breakthroughs in computer vision and is widely used in a variety of applications such as image classification [12], image segmentation [13], and object tracking [14]. Surface defect detection is a technology that detects failures in the appearance of fabric, metal, wooden, and plastic products via image processing technologies [15; 16; 17]. Although the targets can be different, surface defect detection is a feature-extraction problem to identify anomalies that can be distinguished from textures. Algorithms to extract features from textures for detecting surface defects can be defined into four categories: statistical, structural, filter-based, and model-based approaches [18]. Representatively, statistical and filter-based approaches have been prevalent. For example, histogram properties categorized in the statistical approach have been applied to various studies and proved to have high performance even at low cost and effort [19; 20]. Among joint spatial/spatial-frequency methods categorized in filter-based approaches, Gabor transforms (using modulated Gaussian filters) are widely used because they are similar to the human visual system [21]. After the CNN was developed, a filter-kernel-based neural network was proposed, and CNN-based feature-extraction techniques have been rapidly developed in image processing and machine learning research fields. [PERSON] et al. [22] applied a generic deep-learning  
Figure 1: Primary defects of injection molded products.  
approach based on the CNN model for automated surface inspection. [PERSON] et al. [23] performed anomaly detection for industrial surface inspection by learning the deep matrix using a triplet network, which is a modified CNN model. [PERSON] et al. [24] proposed a twofold joint detection model inspired by a CNN for classifying industrial surface inspection. [PERSON] et al. [25] proposed a cascaded autoencoder architecture based on a CNN for segmenting and localizing multiple defects from industrial product data. In addition, many researchers have proposed various powerful CNN-based models to solve image classification problems or defect localization problems for diverse industrial surface defects [26; 27]. Representatively, research on concrete crack detection using CNN-based models has been actively conducted in recent years. [PERSON] et al. [28] applied an ad-hoc faster region-based CNN (faster R-CNN) to distinguish between handwriting scripts and crack on a concrete surface. [PERSON] et al. [29] detected cracks on the concrete surface using a light gradient boosting machine (LightGBM) considered pixel values and geometric shapes. You only look once (YOLO), VGG Net, Inception Net, Mask R-CNN were frequently applied to detect concrete cracks in research on civil and infrastructure engineering [30; 31]. However, research considering the characteristics of injection molding processes and products is yet to receive much attention. In this study, a novel defect classification model is proposed for the injection molding process by redesigning ResNeXt and Deformable ConvNet.