2021/10/22 by Junseok Lee, Lee, Junseok, Jongwon Kim +9
Computer Science · Engineering · #Image Processing and 3D Reconstruction #Industrial Vision Systems and Defect Detection #Manufacturing Process and Optimization #cs.AI #cs.CV
paper · pdf · doi:10.48550/arxiv.2110.11593
4 pages
arxiv created 2021/10/22 · arxiv updated 2021/10/25
This paper proposes a method to automatically detect the key feature parts in a CAD of commercial TV and monitor using a deep neural network. We developed a deep learning pipeline that can detect the injection parts such as hook, boss, undercut and press parts such as DPS, Embo-Screwless, Embo-Burring, and EMBO in the 2D CAD drawing images. We first cropped the drawing to a specific size for the training efficiency of a deep neural network. Then, we use Cascade R-CNN to find the position of injection and press parts and use Resnet-50 to predict the orientation of the parts. Finally, we convert the position of the parts found through the cropped image to the position of the original image. As a result, we obtained detection accuracy of injection and press parts with 84.1% in AP (Average Precision), 91.2% in AR(Average Recall), 72.0% in AP, 87.0% in AR, and orientation accuracy of injection and press parts with 94.4% and 92.0%, which can facilitate the faster design in industrial product design.