Dual-Kernel-Based Aggregated Residual Network for Surface Defect Inspection in Injection Molding Processes
Hwaseop Lee, Kwangyeol Ryu
Abstract
Automated quality inspection has been receiving increasing attention in manufacturing processes. Since the introduction of convolutional neural networks (CNNs), many researchers have attempted to apply CNNs to classification and detection of defect images. However, injection molding processes have not received much attention in this field of research because of product diversity, difficulty in obtaining uniform-quality product images, and short cycle times. In this study, two types of dual-kernel-based aggregated residual networks are proposed by utilizing a fixed kernel and a deformable kernel to detect surface and shape defects of molded products. The aggregated residual network is selected as a backbone, and a fixed-size, deformable kernel is applied for extracting surface and geometric features simultaneously. Comparative studies are conducted by including the existing research using the Weakly Supervised Learning for Industrial Optical Inspection dataset, which is a DAGM dataset. A case study reveals that the proposed method is applicable for inspecting the quality of injection molding products with excellent performance.