Scene-Adaptive Off-Road Detection Using a Monocular Camera

Mei, Jilin; Yu, Yufeng; Zhao, Huijing; Zha, Hongbin

doi:10.1109/tits.2017.2768573

Cited by 27 publications

(13 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Segmentation-based methods formulate the problem as pixel-level segmentation tasks. Some studies [21] assume the region at bottom of images as road data or collect vehicle trajectories as drivable area [22], then label similar regions as roads. Other methods [9][10] depend on fixed road models and make use of hybrid features to extract continuous regions.…”

Section: Related Work a Rule/segmentation-based Methodsmentioning

confidence: 99%

Fine-Grained Off-Road Semantic Segmentation and Mapping via Contrastive Learning

Gao¹,

Hu²,

Zhao³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Section: Related Work a Rule/segmentation-based Methodsmentioning

confidence: 99%

Fine-Grained Off-Road Semantic Segmentation and Mapping via Contrastive Learning

Gao¹,

Hu²,

Zhao³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

“…Mei et al. use an RGB space as the feature space in [21] and an algorithm framework which contains inference and learning is proposed based on this space.…”

Section: Related Workmentioning

confidence: 99%

“…Combined with the information of vanishing points, orientation consistency ratio (OCR) features are used to detect road edges. Mei et al use an RGB space as the feature space in [21] and an algorithm framework which contains inference and learning is proposed based on this space. With the development of deep learning, the powerful feature extraction and representation ability of the deep neural network leads to many methods.…”

Section: Road Detectionmentioning

confidence: 99%

A two‐scaled fully convolutional learning network for road detection

Liang

2021

IET Image Processing

View full text Add to dashboard Cite

This paper aims to detect road regions based on a two‐scaled deep neural network. The information from different scales is helpful to boost the performance of deep learning models, and it is also a widely used strategy in various computer vision applications. In the two‐scaled model, skip‐architecture and fully convolutional layers are used to fuse the low‐level details and high‐level semantic information. It enables to detect the road areas by multi‐scale feature maps from different reception fields. To avoid the redundancy of scale information and the loss of features caused by the pooling layer, the feature maps before the first pooling layer are adopted in our model. By the convolutional kernels, our model can balance the information of two scales automatically. The loss function is also improved, in which the intersection over union (IoU) term is taken into account to guide the model to learn more features on the whole road regions. Comprehensive experiments on three benchmark datasets demonstrate that this approach can reach state‐of‐the‐art performance.

show abstract

“…Some camera-based methods depend on the assumption of global road priors such as road boundaries [7], traffic lanes [8][9] or vanish points [10] [11] . Some other studies do not rely on these assumptions but view the drivable area extraction as a segmentation of road and non-road regions [4] [12]. Furthermore, some stereo camera based approaches [13][14] make use of depth information to help off-road drivable area extraction.…”

Section: Related Workmentioning

confidence: 99%

“…Cameras and LiDAR are two main sensors that provide input data for drivable area extraction tasks. There are many camera-based methods that have already been applied in off-road environments [4]. However, the color or texture features they used are not robust enough in diverse illumination and weather conditions.…”

Section: Introductionmentioning

confidence: 99%

Off-Road Drivable Area Extraction Using 3D LiDAR Data

Gao¹,

Xu²,

Pan³

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

Self Cite

View full text Add to dashboard Cite

We propose a method for off-road drivable area extraction using 3D LiDAR data with the goal of autonomous driving application. A specific deep learning framework is designed to deal with the ambiguous area, which is one of the main challenges in the off-road environment. To reduce the considerable demand for human-annotated data for network training, we utilize the information from vast quantities of vehicle paths and auto-generated obstacle labels. Using these autogenerated annotations, the proposed network can be trained using weakly supervised or semi-supervised methods, which can achieve better performance with fewer human annotations. The experiments on our dataset illustrate the reasonability of our framework and the validity of our weakly and semi-supervised methods.

show abstract

Scene-Adaptive Off-Road Detection Using a Monocular Camera

Cited by 27 publications

References 33 publications

Fine-Grained Off-Road Semantic Segmentation and Mapping via Contrastive Learning

Fine-Grained Off-Road Semantic Segmentation and Mapping via Contrastive Learning

A two‐scaled fully convolutional learning network for road detection

Off-Road Drivable Area Extraction Using 3D LiDAR Data

Contact Info

Product

Resources

About