Recent studies show edge computing-based road anomaly detection systems which
may also conduct data collection simultaneously. However, the edge computers
will have small data storage but we need to store the collected audio samples
for a long time in order to update existing models or develop a novel method.
Therefore, we should consider an approach for efficient storage management
methods while preserving high-fidelity audio. A hardware-perspective approach,
such as using a low-resolution microphone, is an intuitive way to reduce file
size but is not recommended because it fundamentally cuts off high-frequency
components. On the other hand, a computational file compression approach that
encodes collected high-resolution audio into a compact code should be
recommended because it also provides a corresponding decoding method. Motivated
by this, we propose a way of simple yet effective pre-trained autoencoder-based
data compression method. The pre-trained autoencoder is trained for the purpose
of audio super-resolution so it can be utilized to encode or decode any
arbitrary sampling rate. Moreover, it will reduce the communication cost for
data transmission from the edge to the central server. Via the comparative
experiments, we confirm that the zero-shot audio compression and decompression
highly preserve anomaly detection performance while enhancing storage and
transmission efficiency.

提出了一种基于预训练自编码器的数据压缩方法，旨在提高边缘计算中基于音频的道路异常检测系统的存储和传输效率。经过对比实验，确认零样本音频压缩具有高度保留异常检测性能的同时，提升了存储和传输效率。

具有零射击数据压缩的边缘存储管理配方用于路面异常检测

Edge Storage Management Recipe with Zero-Shot Data Compression for Road  Anomaly Detection

Joint detection of drivable areas and road anomalies is very important for
mobile robots. Recently, many semantic segmentation approaches based on
convolutional neural networks (CNNs) have been proposed for pixel-wise drivable
area and road anomaly detection. In addition, some benchmark datasets, such as
KITTI and Cityscapes, have been widely used. However, the existing benchmarks
are mostly designed for self-driving cars. There lacks a benchmark for ground
mobile robots, such as robotic wheelchairs. Therefore, in this paper, we first
build a drivable area and road anomaly detection benchmark for ground mobile
robots, evaluating the existing state-of-the-art single-modal and data-fusion
semantic segmentation CNNs using six modalities of visual features.
Furthermore, we propose a novel module, referred to as the dynamic fusion
module (DFM), which can be easily deployed in existing data-fusion networks to
fuse different types of visual features effectively and efficiently. The
experimental results show that the transformed disparity image is the most
informative visual feature and the proposed DFM-RTFNet outperforms the
state-of-the-arts. Additionally, our DFM-RTFNet achieves competitive
performance on the KITTI road benchmark. Our benchmark is publicly available at
this https URL

本文提出了一个针对地面移动机器人的可行驶区域和路面异常检测基准，评估了现有单模和数据融合语义分割 CNN 的性能，并提出了一个名为动态融合模块（DFM）的新模块来有效和高效地融合不同类型的视觉特征。实验结果表明，变换视差图是最具信息量的视觉特征，提出的 DFM-RTFNet 在 KITTI 路况基准上表现出色，并在公开数据集上可用。