# Dual-Kernel-Based Aggregated Residual Network for Surface Defect Inspection in Injection
## 1 Introduction  
Quality inspection in manufacturing industries is one of the essential processes that can reduce the risk and cost of failure of delivery to customers. Manufacturing companies conduct a total inspection when the product is a part directly used by customers and related to human safety. Most small- and medium-sized enterprises (SMEs) that produce parts with injection molding operations continue to inspect such parts manually to classify defective products. Manual inspection is subject to human error caused by fatigue in continuous repetitive work [1]. In such an environment, injection molding companies consume enormous time, effort, and cost for manual inspection. To solve this problem, automated quality inspection for detecting defects has been developed by many researchers to reduce human effort during inspection processes [2; 3].  
Injection molding is a crucial underlying technology that can produce a wide variety of products that differ significantly in size, complexity, and application [4]. Injection molding companies must conduct a total inspection of products directly related to customer safety to ensure the proper functionality and appearance of the products. The primary defect types in injection molding products are unformed shapes (e.g., bubbles, flash, and short shots) and poor surface appearance (e.g., burn marks, flow lines, sink marks, and jetting). As depicted in Figure 1, most defects in injection molding products can be found on the surface of the parts. Therefore, automated visual inspection using image processing techniques such as feature extraction, classification, and object detection is applicable [5; 6; 7].  
However, the application of image processing techniques for automated visual inspection is difficult because of the characteristics of the injection molding process. First, multiple products are usually produced in one cycle in one mold. Because the mold ejects several products during one cycle time, obtaining uniform-quality image data of multiple parts in the manufacturing process is challenging. Second, the injection process has a short cycle time. According to the aforementioned characteristics, the injection molding process requires more effective and faster feature-extraction methodologies for a visual inspection system. In this study, a dual-kernel-based aggregated residual network (ResNeXt) is proposed to solve the defect detection problem for the injection molding process. ResNeXt has been modified accordingly to achieve better detection performance while decreasing network complexity; this forms the backbone of the proposed model [8]. In addition, a deformable kernel is applied for robustness in processing scaled and rotated defect images [9; 10].  
The remainder of this paper is organized as follows. Section 2 presents a literature review on surface defect detection, ResNeXt, and the Deformable convolution network (Deformable ConvNet), which are core concepts behind the defect-detection model proposed in this study. Section 3 describes the details of the proposed methodologies and verification experiments using the DAGM 2007 competition dataset (Weakly Supervised Learning for Industrial Optical Inspection). Here, DAGM stands for Deutsche Arbeitsgemeinschaft fur Musterekennung which means that the German chapter of the International Association for Pattern Recognition. A case study of an experiment on defect inspection using real data of an injection molding product is presented in Section 4, and Section 5 presents concluding remarks.