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http://dx.doi.org/10.3745/KTCCS.2022.11.11.411

Shipping Container Load State and Accident Risk Detection Techniques Based Deep Learning  

Yeon, Jeong Hum (부산항만공사 항만 R&D실)
Seo, Yong Uk ((주)서안에스앤씨)
Kim, Sang Woo ((주)서안에스앤씨)
Oh, Se Yeong (동의대학교 IT융합학과)
Jeong, Jun Ho (동의대학교 IT융합학과)
Park, Jin Hyo (동의대학교 IT융합학과)
Kim, Sung-Hee (동의대학교 산업융합시스템공학부)
Youn, Joosang (동의대학교 산업ICT기술공학과)
Publication Information
KIPS Transactions on Computer and Communication Systems / v.11, no.11, 2022 , pp. 411-418 More about this Journal
Abstract
Incorrectly loaded containers can easily knock down by strong winds. Container collapse accidents can lead to material damage and paralysis of the port system. In this paper, We propose a deep learning-based container loading state and accident risk detection technique. Using Darknet-based YOLO, the container load status identifies in real-time through corner casting on the top and bottom of the container, and the risk of accidents notifies the manager. We present criteria for classifying container alignment states and select efficient learning algorithms based on inference speed, classification accuracy, detection accuracy, and FPS in real embedded devices in the same environment. The study found that YOLOv4 had a weaker inference speed and performance of FPS than YOLOv3, but showed strong performance in classification accuracy and detection accuracy.
Keywords
Shipping Container; YOLOv4; Deep Learning; Object Detection;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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