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Open Access

ARTICLE

A Real Time YOLO Based Container Grapple Slot Detection and Classification System

Chen-Chiung Hsieh1,*, Chun-An Chen1, Wei-Hsin Huang2
1 Department of Computer Science and Engineering, Tatung University, Taipei City, 104, Taiwan
2 Department of Digital Media Design, Tatung University, Taipei City, 104, Taiwan
* Corresponding Author: Chen-Chiung Hsieh. Email: email
(This article belongs to the Special Issue: Development and Application of Deep Learning based Object Detection)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.072514

Received 28 August 2025; Accepted 04 November 2025; Published online 28 November 2025

Abstract

Container transportation is pivotal in global trade due to its efficiency, safety, and cost-effectiveness. However, structural defects—particularly in grapple slots—can result in cargo damage, financial loss, and elevated safety risks, including container drops during lifting operations. Timely and accurate inspection before and after transit is therefore essential. Traditional inspection methods rely heavily on manual observation of internal and external surfaces, which are time-consuming, resource-intensive, and prone to subjective errors. Container roofs pose additional challenges due to limited visibility, while grapple slots are especially vulnerable to wear from frequent use. This study proposes a two-stage automated detection framework targeting defects in container roof grapple slots. In the first stage, YOLOv7 is employed to localize grapple slot regions with high precision. In the second stage, ResNet50 classifies the extracted slots as either intact or defective. The results from both stages are integrated into a human–machine interface for real-time visualization and user verification. Experimental evaluations demonstrate that YOLOv7 achieves a 99% detection rate at 100 frames per second (FPS), while ResNet50 attains 87% classification accuracy at 34 FPS. Compared to some state of the arts, the proposed system offers significant speed, reliability, and usability improvements, enabling efficient defect identification and visual reconfirmation via the interface.

Keywords

Container grapple slot detection; defect classification; deep learning; two-stage; YOLO

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