Yeon Jeong Hum, Seo Yong Uk, Kim Sang Woo, Oh Se Yeong, Jeong Jun Ho, Park Jin Hyo, Kim Sung-Hee, Youn Joosang

KIPS Transactions on Computer and Communication Systems, Vol. 11, No. 11, pp. 411-418, Nov. 2022
https://doi.org/10.3745/KTCCS.2022.11.11.411,   PDF Download:
Keywords: Shipping Container, Yolov4, Deep Learning, Object Detection
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.

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