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.

中文翻译：

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基于迁移学习的 CNN 集装箱多类型损坏检测

由于机械操作和自然因素的反复承受，容器在使用过程中会遭受各种类型的损坏。采用有效的集装箱损坏检测方法，对延长使用寿命和使用功能起着至关重要的作用。本文提出了一种基于迁移学习和MobileNetV2的集装箱多类型损坏检测模型。此外，还建立了一个包含九种典型集装箱损坏类型的数据集。为保证模型的有效性和实用性，我们在实际港口环境中进行了测试验证。结果表明，该模型可以识别多种类型的集装箱损坏。与现有模型相比，本文提出的破损检测模型能够保证各类集装箱破损的识别效果，更适合实际的集装箱检测情况。该方法可为港口集装箱管理提供一种新的破损检测思路。