Yu Xiaodong, Kuan Ta-Wen, Tseng Shih-Pang, Chen Ying, Chen Shuo, Wang Jhing-Fa, Gu Yuhang, Chen Tuoli
School of Information Science and Technology, Sanda University, No. 2727 Jinhai Road, Shanghai Pudong District, Shanghai 201209, China.
School of Software and Big Data, Changzhou College of Information Technology, Changzhou 213164, China.
Entropy (Basel). 2023 Jul 19;25(7):1085. doi: 10.3390/e25071085.
Road segmentation is beneficial to build a vision-controllable mission-oriented self-driving bot, e.g., the Self-Driving Sweeping Bot, or SDSB, for working in restricted areas. Using road segmentation, the bot itself and physical facilities may be protected and the sweeping efficiency of the SDSB promoted. However, roads in the real world are generally exposed to intricate noise conditions as a result of changing weather and climate effects; these include sunshine spots, shadowing caused by trees or physical facilities, traffic obstacles and signs, and cracks or sealing signs resulting from long-term road usage, as well as different types of road materials, such as cement or asphalt; all of these factors greatly influence the effectiveness of road segmentation. In this work, we investigate the extension of Primordial U-Net by the proposed EnRDeA U-Net, which uses an input channel applying a Residual U-Net block as an encoder and an attention gate in the output channel as a decoder, to validate a dataset of intricate road noises. In addition, we carry out a detailed analysis of the nets' features and segmentation performance to validate the intricate noises dataset on three U-Net extensions, i.e., the Primordial U-Net, Residual U-Net, and EnRDeA U-Net. Finally, the nets' structures, parameters, training losses, performance indexes, etc., are presented and discussed in the experimental results.
道路分割有助于构建一个视觉可控的面向任务的自动驾驶机器人，例如用于在受限区域工作的自动驾驶清扫机器人（Self-Driving Sweeping Bot，简称SDSB）。通过道路分割，可以保护机器人自身和物理设施，并提高SDSB的清扫效率。然而，由于天气和气候影响的变化，现实世界中的道路通常会受到复杂的噪声条件影响；这些噪声包括阳光斑点、树木或物理设施造成的阴影、交通障碍物和标志，以及长期道路使用导致的裂缝或密封标志，还有不同类型的道路材料，如水泥或沥青；所有这些因素都极大地影响了道路分割的有效性。在这项工作中，我们研究了通过提出的EnRDeA U-Net对原始U-Net的扩展，该扩展在输入通道使用一个应用残差U-Net块的编码器，并在输出通道使用一个注意力门作为解码器，以验证一个包含复杂道路噪声的数据集。此外，我们对这些网络的特征和分割性能进行了详细分析，以在三个U-Net扩展（即原始U-Net、残差U-Net和EnRDeA U-Net）上验证复杂噪声数据集。最后，在实验结果中展示并讨论了这些网络的结构、参数、训练损失、性能指标等。
