Automated Road-Marking Segmentation via a Multiscale Attention-Based Dilated Convolutional Neural Network Using the Road Marking Dataset
Automated Road-Marking Segmentation via a Multiscale Attention-Based Dilated Convolutional Neural Network Using the Road Marking Dataset
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DOI：
10.3390/rs14184508
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
Junjie Wu-;Wen Liu;Y. Maruyama
中科院分区：
文献类型：
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作者：
Junjie Wu-;Wen Liu;Y. Maruyama
道路标线，包括道路车道和标志性道路标线，可以向自动驾驶汽车传达丰富的引导信息。然而，近年来的研究较少关注道路标志的识别。在这项研究中，道路标记分割数据集命名为RMD（道路标记数据集），以弥补数据集的缺乏和现有数据集的局限性。此外，我们提出了一种新的基于多尺度注意力的扩张卷积神经网络（MSA-DCNN）来解决所提出的RMD。该方法采用多尺度注意力合并相邻多尺度输入的加权输出，并采用扩张卷积来捕获空间上下文信息。性能分析表明，所提出的MSA-DCNN通过结合多尺度注意力和扩张卷积产生最好的结果。此外，该方法获得了74.88%的mIoU，这是对现有技术的显着改进。
Road markings, including road lanes and symbolic road markings, can convey abundant guidance information to autonomous driving cars. However, recent works have paid less attention to the recognition of symbolic road markings compared with road lanes. In this study, a road-marking-segmentation dataset named the RMD (Road Marking Dataset) is introduced to compensate for the lack of datasets and the limitations of the existing datasets. Furthermore, we propose a novel multiscale attention-based dilated convolutional neural network (MSA-DCNN) to tackle the proposed RMD. The proposed method employs multiscale attention to merge the weighting outputs of adjacent multiscale inputs, and dilated convolution to capture spatial-context information. The performance analysis shows that the proposed MSA-DCNN yields the best results by combining multiscale attention and dilated convolution. Additionally, the proposed method gains the mIoU of 74.88%, which is a significant improvement over the existing techniques.