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Kazumoto Tanaka
Kindai University
Computer Science & IT Control & Systems Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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A Simple Method to Calculate Positions in Pose Tracking to Verify Work Procedures Kazumoto Tanaka
Engineering Science Letter Vol. 2 No. 02 (2023): Engineering Science Letter
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/esl.v2i02.313

Abstract

Although manufacturing processes are becoming increasingly automated, many factories still rely on manual operations. In such facilities, there is a strong need to automatically detect human errors by checking whether specified procedures are being followed. For this purpose, studies have been conducted on utilizing deep neural network (DNN) based three-dimensional (3D) human pose-tracking methods to examine work procedures. However, most of these techniques require a high-end computer equipped with a graphics processing unit (GPU). On the other hand, in this study, we adopt MediaPipe Pose, a lightweight pose estimation network provided by Google, to perform pose tracking on a low-end personal computer (PC) to enable such systems to be deployed in small factories. However, MediaPipe Pose cannot track the location of a human body because it estimates poses in a coordinate system with the waist as the origin (that is, in a root‑relative coordinate system). Therefore, in this study, we developed a method to obtain the absolute coordinates of the root with a simple calculation. The results of an experimental evaluation show that the computational load of the proposed approach is negligible, and the repeatability of the estimation sufficed to evaluate a given operator's work on a predetermined working path. Therefore, the proposed methods enables work procedures to be checked using MediaPipe Pose on a low-end PC.
Detection Method of Concave Defect on Specular Surfaces Based on Swin Transformer Kazumoto Tanaka
Engineering Science Letter Vol. 4 No. 01 (2025): Engineering Science Letter
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/IISTR.esl.00743

Abstract

Shallow concave defects on mirrored surfaces are difficult to detect automatically. This paper proposes a defect detection method using a deep neural network (DNN) that learns the presence or absence of distortion in the image of a stripe pattern reflected on a mirror surface. The Swin Transformer is used as the DNN to capture global features of the edges of the reflection. In the manufacturing process, the occurrence of defects is minimized, so it is difficult to collect enough defect images for training purposes. Therefore, in this paper, we show how to generate a large number of images of stripe pattern reflections using an optical simulation method. Our Swin Transformer showed high detection performance in defect detection experiments using actual mirrored parts.
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