Vision-based automatic noise-barrier inspection of high-speed railway, instead of manual patrol, remains a great challenge. Even though many supervised learning-based methods have been developed, massive redundant video frames and scarce defective samples are the main obstacles to leverage the performance of the noise-barrier inspection task. To tackle the problems, we present a novel Vision-based Noise-barrier Inspection System (VNIS), which is deployed on the bullet train to inspect the noise-barrier defects by using motion video. VNIS uses the proposed panorama generation model based on motion video to obtain panoramic images from massive redundant video sequences. Then, we employ a self-supervised learning deep network to solve the problem of the scarce defective samples. Comprehensive experiments are conducted on a large-scale video dataset of bullet train. VNIS yields competitive performance on noise-barrier defects inspection. Specifically, an average accuracy of 99.14% is achieved for noise-barrier defects inspection.

高速铁路基于视觉的自动噪声屏障检查，而不是人工巡逻，仍然是一个巨大的挑战。尽管已经开发了许多基于监督学习的方法，但大量冗余视频帧和稀缺的缺陷样本是利用噪声屏障检测任务性能的主要障碍。为了解决这些问题，我们提出了一种新颖的基于视觉的噪声屏障检测系统（VNIS），该系统部署在子弹头列车上，通过使用运动视频来检测噪声屏障缺陷。VNIS 使用提出的基于运动视频的全景生成模型从海量冗余视频序列中获取全景图像。然后，我们采用自监督学习深度网络来解决稀缺缺陷样本的问题。在子弹头列车的大规模视频数据集上进行了综合实验。VNIS 在噪声屏障缺陷检测方面具有竞争力的性能。具体来说，噪声屏障缺陷检测的平均准确率为 99.14%。