- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Aerial LaneNet: Lane-Marking Semantic Segmentation in Aerial Imagery Using Wavelet-Enhanced Cost-Sensitive Symmetric Fully Convolutional Neural Networks
摘要： The knowledge about the placement and appearance of lane markings is a prerequisite for the creation of maps with high precision, necessary for autonomous driving, infrastructure monitoring, lanewise traffic management, and urban planning. Lane markings are one of the important components of such maps. Lane markings convey the rules of roads to drivers. While these rules are learned by humans, an autonomous driving vehicle should be taught to learn them to localize itself. Therefore, accurate and reliable lane-marking semantic segmentation in the imagery of roads and highways is needed to achieve such goals. We use airborne imagery that can capture a large area in a short period of time by introducing an aerial lane marking data set. In this paper, we propose a symmetric fully convolutional neural network enhanced by wavelet transform in order to automatically carry out lane-marking segmentation in aerial imagery. Due to a heavily unbalanced problem in terms of a number of lane-marking pixels compared with background pixels, we use a customized loss function as well as a new type of data augmentation step. We achieve a high accuracy in pixelwise localization of lane markings compared with the state-of-the-art methods without using the third-party information. In this paper, we introduce the first high-quality data set used within our experiments, which contains a broad range of situations and classes of lane markings representative of today’s transportation systems. This data set will be publicly available, and hence, it can be used as the benchmark data set for future algorithms within this domain.
关键词： Aerial imagery,wavelet transform,autonomous driving,traffic monitoring,remote sensing,fully convolutional neural networks (FCNNs),lane-marking segmentation,infrastructure monitoring,mapping
更新于2025-09-09 09:28:46
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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - A Fast Local Analysis by Thresholding applied to image matching
摘要： Keystructures extraction and matching are key steps in computer vision. Many fields of application need large image acquisition and fast extraction of finest structures. In this study, we focus on situations where existing local feature extractors give not enough satisfying results concerning both accuracy and time processing. Among good illustrations, we can quote short-line extraction in local weakly-contrasted images. We propose a new Fast Local Analysis by threSHolding (FLASH) designed to process large images under hard time constraints. We use "micro-line" points as key feature. These are used for shape reconstruction (like lines) and local signature design. We apply FLASH on the field of concrete infrastructure monitoring where robots and UAVs are more and more used for automated defect detection (like cracks). For large concrete surfaces, there are several hard constraints such as the computational time and the reliability. Results show us that the computations are faster than several existing algorithms in image matching and FLASH has invariance to rotation, partial occlusion, and scale range from 0.7 to 1.4 without scale-space exploration.
关键词： concrete infrastructure monitoring,crack detection,computer vision,feature extraction,FLASH,image matching
更新于2025-09-04 15:30:14