Paper Info
Title
A Single Shot Multibox Detector Based On Welding Operation Method For Biometrics Recognition In Smart Cities
Abstract
As enhance of safety requirement in the smart cities, biometrics recognition, as an approach for society safety, has been greatly researched and developed. The identification of working status of welders will help judge whether they are wearing personal protective equipment correctly. We proposed an improved algorithm based on SSD (Single Shot Multibox Detector) that can identify three mainstream manual welding methods including SMAW (shielded metal arc welding), GMAW (gas metal arc welding) and TIG (tungsten inert gas), which has never been researched before and can promote the intelligentization of welding monitoring to construct smart cities. The improvement includes two parts. Firstly, the backbone of SSD is replaced with MobileNetV3. Then, a feature fusion module is added to enhance the information of low-level feature maps to improve detection accuracy. The experimental results of our welding behavior detector show that, the mAP is 87.45%, detection speed is 25FPS, and parameter memory is 87.5 MB, which has a relatively excellent performance considering speed, accuracy and memory. (c) 2020 Elsevier B.V. All rights reserved.
Year
DOI
Venue
2020
10.1016/j.patrec.2020.10.016
PATTERN RECOGNITION LETTERS
Keywords
DocType
Volume
Deep learning, Object detection, Biometrics recognition, SSD, Welding
Journal
140
ISSN
Citations 
PageRank 
0167-8655
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Hongzhi Lu140.79
Changfan Li200.34
Weiming Chen321.78
Zijie Jiang400.34