A visual detection method for conveyor belt misalignment based on the improved YOLACT network-Reference-Cited by-同舟云学术

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A visual detection method for conveyor belt misalignment based on the improved YOLACT network

Published:2023-10-19 Issue: Volume: Page:1-12
ISSN:0272-6351
Container-title:Particulate Science and Technology
language:en
Short-container-title:Particulate Science and Technology

Author:

Jin Chenglong¹,Yuan Jianming¹²,Shen Jiahe¹³^ORCID,Wheeler Craig³,Liu Zhiyuan¹,Yan Jiahao¹

Affiliation:

1. School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan, PR China

2. Hainan Institute, Wuhan University of Technology, Hainan, PR China

3. The Centre for Bulk Solids and Particulate Technologies, The University of Newcastle, Callaghan, Australia

Funder

National Natural Science Foundation of China

Hainan Institute at Wuhan University of Technology

Publisher

Informa UK Limited

Subject

General Chemical Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/02726351.2023.2268567

Reference41 articles.

1. Two Novel Models for Traffic Sign Detection Based on YOLOv5s

2. Bolya, D., C. Zhou, F. Xiao, and Y. J. Lee. 2019. YOLACT: Real-time instance segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 9157–66. Seoul (South Korea): IEEE.

3. YOLACT++ Better Real-Time Instance Segmentation

4. Types and causes of damage to the conveyor belt – Review, classification and mutual relations

5. Health monitoring of a conveyor belt system using machine vision and real-time sensor data

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