Multitype Damage Detection of Container Using CNN Based on Transfer Learning

Zixin Wang,Qingcheng Zeng,Jing Gao,Yuhui Sun,Mahmoud Mesbah

doi:10.1155/2021/5395494

Zixin Wang, Qingcheng Zeng + Show 3 more

Open Access

https://doi.org/10.1155/2021/5395494

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Abstract

Due to the repeated bearing of mechanical operations and natural factors, the container will suffer various types of damage during use. Adopting effective container damage detection methods plays a vital role in prolonging the service life and using function. This paper proposes a multitype damage detection model for containers based on transfer learning and MobileNetV2. In addition, a data set containing nine typical types of container damage is established. To ensure the validity and practicability of the model, we conducted tests and verifications in the actual port environment. The results show that the model can identify multiple types of container damage. Compared with the existing models, the damage detection model proposed in this paper can ensure the identification effect of various types of container damage, which is more suitable for the actual container detection situation. This method can provide a new idea of damage detection for container management in ports.

Highlights

As a protective barrier for cargo, containers are an indispensable part of modern logistics
Based on the transfer learning and MobileNetV2, this paper proposes a multitype damage detection model for containers
To ensure the validity and practicability of the model, we conducted tests and verifications in the actual port environment. e results show that the model can identify multiple types of container damage

Summary

Introduction

As a protective barrier for cargo, containers are an indispensable part of modern logistics. Erefore, we propose a multitype damage detection method for containers based on transfer learning. Based on the health structure detection of buildings, Han and Tang [7] used a hybrid data enhancement method combined with the Faster R-CNN recognition framework to achieve a 5.14% improvement in the average accuracy of damage detection. Maeda [8] addressed road damage identification and detection and proposed a detection model that can identify eight types of damage. Promising experimental results have been obtained, illustrating the research value and feasibility of the damage detection method based on deep learning. Based on the transfer learning and MobileNetV2, this paper proposes a multitype damage detection model for containers. Compared with the existing models, the damage detection model proposed in this paper can identify multiple types of container damage.

Related Works

Multitype of Container Damage

Results and Discussion

Conclusions and Prospects