  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.
