Chip Contour Detection Based on Real-time Image Sensing and Recognition

被引：1

作者：

Chang, Bao-Rong ^{[1
]}

Tsai, Hsiu-Fen ^{[2
]}

Hsieh, Chia-Wei ^{[1
]}

Chen, Mo-Lan ^{[3
]}

机构：

[1] Natl Univ Kaohsiung, Dept Comp Sci & Informat Engn, 700 Kaohsiung Univ Rd, Kaohsiung 811, Taiwan

[2] Kaohsiung Med Univ, Dept Fragrance & Cosmet Sci, 100 Shih Chuan 1st Rd, Kaohsiung 80708, Taiwan

[3] NXP Semicond Taiwan Ltd, 10 Jing 5th Rd, Kaohsiung 811, Taiwan

来源：

SENSORS AND MATERIALS | 2022年 / 34卷 / 03期

关键词：

deep learning; Jetson Nano; object tracking; real-time image sensing and recognition; GSEH; YOLOv5; attention mechanism; SENet; GhostNet;

D O I：

10.18494/SAM3378

中图分类号：

TH7 [仪器、仪表];

学科分类号：

0804 ; 080401 ; 081102 ;

摘要：

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.

引用

页码：1077 / 1089

页数：13

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