Deformation And Penetration Hybrid Detection-Net For Parcels Inspection In Industrial Supply Chain-Reference-Cited by-同舟云学术

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Deformation And Penetration Hybrid Detection-Net For Parcels Inspection In Industrial Supply Chain

Published:2024-04-14 Issue: Volume: Page:5815-5819
ISSN:
Container-title:ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Chen Zhi¹,Du Cuifeng²,Huang Xiujie¹,Lin Zelong¹,Zhou Yuyu³,Guan Quanlong¹,Li Zhefu¹,Lv Shuanghuan¹,Wu Xiaofeng⁴,Zhuang Xiaotian⁵

Affiliation:

1. Jinan University,College of Information Science and Technology,Guangzhou,China

2. Cetc Potevio Science&Technology Co., Ltd,Guangzhou,China

3. Jinan University,School of Intelligent Systems Science and Engineering,Zhuhai,China

4. Guangzhou Polytechnic of Sports,Guangzhou,China

5. Beijing JD Zhenshi Information Technology Co., Ltd,Beijing,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10445798/10445803/10448282.pdf?arnumber=10448282

Reference19 articles.

1. Fatigue failure and Grms–N curve of corrugated paperboard box

2. Few-shot Learning for Quality Detection of Logistical Parcels

3. A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets

4. Efficient Parcel Damage Detection via Faster R-CNN: A Deep Learning Approach for Logistical Parcels’ Automated Inspection

5. Gradient-based learning applied to document recognition

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