Deep learning has achieved promising results in pavement distress detection. However, the training model's effectiveness varies according to the data and scenarios acquired by different camera types and their installation positions. It is time consuming and labor intensive to recollect labeled data and retrain a new model every time the scene changes. In this paper, we propose a transfer learning pipeline to address this problem, which enables a distress detection model to be applied to other untrained scenarios. The framework consists of two main components: data transfer and model transfer. The former trains a generative adversarial network to transfer existing image data into a new scene style. Then, attentive CutMix and image melding are applied to insert distress annotations to synthesize the new scene's labeled data. After data expansion, the latter step transfers the feature extracted by the existing model to the detection application of the new scene through domain adaptation. The effects of varying degrees of knowledge transfer are also discussed. The proposed method is evaluated on two data sets from two different scenes with more than 40,000 images totally. This method can reduce the demand for training data by at least 25% when the model is applied in a new scene. With the same number of training images, the proposed method can improve the model accuracy by 26.55%.

中文翻译：

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一种新型迁移学习框架的跨场景路面遇险检测

深度学习在路面遇险检测方面取得了可喜的成果。但是，训练模型的有效性根据不同相机类型及其安装位置获取的数据和场景而有所不同。每次场景变化时，重新收集标记数据并重新训练新模型既费时又费力。在本文中，我们提出了一个转移学习管道来解决这个问题，它使遇险检测模型能够应用于其他未经训练的场景。该框架由两个主要组件组成：数据传输和模型传输。前者训练生成对抗网络将现有图像数据转换为新的场景风格。然后，细心的 CutMix 和图像融合被应用于插入遇险注释以合成新场景的标记数据。数据扩展后，后一步通过域自适应将现有模型提取的特征转移到新场景的检测应用中。还讨论了不同程度的知识转移的影响。所提出的方法在来自两个不同场景的两个数据集上进行了评估，总共超过 40,000 张图像。当模型应用于新场景时，这种方法可以减少至少 25% 的训练数据需求。在训练图像数量相同的情况下，所提出的方法可以将模型准确率提高 26.55%。所提出的方法在来自两个不同场景的两个数据集上进行了评估，总共超过 40,000 张图像。当模型应用于新场景时，这种方法可以减少至少 25% 的训练数据需求。在训练图像数量相同的情况下，所提出的方法可以将模型准确率提高 26.55%。所提出的方法在来自两个不同场景的两个数据集上进行了评估，总共超过 40,000 张图像。当模型应用于新场景时，这种方法可以减少至少 25% 的训练数据需求。在训练图像数量相同的情况下，所提出的方法可以将模型准确率提高 26.55%。