Wet weather makes water film over the road and that film causes lower friction between tire and road surface. When a vehicle passes the low-friction road, the accident can occur up to 35% higher frequency than a normal condition road. In order to prevent accidents as above, identifying the road condition in real-time is essential. Thus, we propose a convolutional auto-encoder-based anomaly detection model for taking both less computational resources and achieving higher anomaly detection performance. The proposed model adopts a non-compression method rather than a conventional bottleneck structured auto-encoder. As a result, the computational cost of the neural network is reduced up to 1 over 25 compared with the conventional models, and the anomaly detection performance is improved by up to 7.72%. Thus, we conclude the proposed model as a cutting-edge algorithm for real-time anomaly detection. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

中文翻译：

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用于驱动基于噪声的路面异常检测的高效非压缩自动编码器

潮湿的天气会在道路上形成水膜，并且该水膜会降低轮胎和路面之间的摩擦力。当车辆通过低摩擦路面时，事故发生的频率可能比正常路面高出 35%。为了防止上述事故，实时识别道路状况至关重要。因此，我们提出了一种基于卷积自动编码器的异常检测模型，用于占用更少的计算资源并实现更高的异常检测性能。所提出的模型采用非压缩方法而不是传统的瓶颈结构自动编码器。结果，与传统模型相比，神经网络的计算成本降低了 25 分之 1，异常检测性能提高了 7.72%。因此，我们将所提出的模型总结为实时异常检测的前沿算法。© 2022 日本电气工程师学会。由 Wiley Periodicals LLC 出版。