Research progress on visual detection technology for port container handling equipment

FEI Fei; CUI Guohua; YANG Mei; WANG Yalan; WEI Dan

doi:10.12299/jsues.24-0191

Volume 40 Issue 1

Mar. 2026

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FEI Fei, CUI Guohua, YANG Mei, WANG Yalan, WEI Dan. Research progress on visual detection technology for port container handling equipment[J]. Journal of Shanghai University of Engineering Science, 2026, 40(1): 81-87. doi: 10.12299/jsues.24-0191

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FEI Fei, CUI Guohua, YANG Mei, WANG Yalan, WEI Dan. Research progress on visual detection technology for port container handling equipment[J]. Journal of Shanghai University of Engineering Science, 2026, 40(1): 81-87. doi: 10.12299/jsues.24-0191

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Research progress on visual detection technology for port container handling equipment

doi: 10.12299/jsues.24-0191

FEI Fei^1
,,
CUI Guohua^{1
,
,},
YANG Mei²,
WANG Yalan¹,
WEI Dan¹

1.
School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
2.
Shanghai International Port (Group) Limited Company, Shanghai 200082, China

Received Date: 2024-06-29
Available Online: 2026-05-27
Publish Date: 2026-03-01

Abstract

Abstract

A systematic review was presented on vision-based detection techniques for unmanned container management, covering image preprocessing, object detection, and recognition. Detection algorithms were classified into “feature + classifier” models and deep learning models, with their application objects, advantages, and limitations summarized. Challenges and trends in typical port scenarios were discussed, along with research progress in key machine vision techniques for autonomous container reach stackers based on engineering practice. Current findings show that consensus has been reached on the integration of feature engineering and deep learning, as well as on the trade-offs between real-time perception and detection accuracy. However, limitations still exist under conditions such as illumination changes, occlusion, multi-scale detection, and scarce labeled data. Future research should focus on lightweight network design, multi-sensor fusion, and domain-adaptive transfer learning to facilitate engineering deployment and intelligent upgrading of port container detection.
- port automation,
- container handling equipment,
- visual detection,
- image processing,
- object recognition

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References(44)

References

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