S&M2874 Research Paper of Special Issue
Published in advance: June 15, 2021
Published: March 17, 2022
Chip Contour Detection Based on Real-time Image Sensing and Recognition [PDF]
Bao-Rong Chang, Hsiu-Fen Tsai, Chia-Wei Hsieh, and Mo-Lan Chen
(Received March 28, 2021; Accepted May 26, 2021)
Keywords: deep learning, Jetson Nano, object tracking, real-time image sensing and recognition, GSEH-YOLOv5, attention mechanism, SENet, GhostNet
In this study, the GSEH-YOLOv5 (GhostNet and SENet included in Head-YOLOv5) algorithm is used to realize real-time object tracking and image sensing and recognition on the Jetson Nano embedded platform. The purpose is to instantly detect the appearance contour of the chip inside the chip slot. As soon as our system detects the damaged chip, a warning is generated, and the correct location of the damaged chip in the chip slot is labeled. After that, the operator immediately removes the damaged chip to prevent the next chip from being damaged. Finally, we also analyze and compare the performance between the improved GSEH-YOLOv5 algorithm and the traditional YOLOv5 algorithm to verify that the proposed method has the better performance.Corresponding author: Hsiu-Fen Tsai, Chia-Wei Hsieh
This work is licensed under a Creative Commons Attribution 4.0 International License.
Cite this article
Bao-Rong Chang, Hsiu-Fen Tsai, Chia-Wei Hsieh, and Mo-Lan Chen, Chip Contour Detection Based on Real-time Image Sensing and Recognition, Sens. Mater., Vol. 34, No. 3, 2022, p. 1077-1089.