Road Detection Task Research Articles

Road Segmentation in High-Resolution Images Using Deep Residual Networks

Automatic road detection from remote sensing images is a vital application for traffic management, urban planning, and disaster management. The presence of occlusions like shadows of buildings, trees, and flyovers in high-resolution images and miss-classifications in databases create obstacles in the road detection task. Therefore, an automatic road detection system is required to detect roads in the presence of occlusions. This paper presents a deep convolutional neural network to address the problem of road detection, consisting of an encoder-decoder architecture. The architecture contains a U-Network with residual blocks. U-Network allows the transfer of low-level features to the high-level, helping the network to learn low-level details. Residual blocks help maintain the network's training performance, which may deteriorate due to a deep network. The encoder and decoder structures generate a feature map and classify pixels into road and non-road classes, respectively. Experimentation was performed on the Massachusetts road dataset. The results showed that the proposed model gave better accuracy than current state-of-the-art methods.

Enabling Road Detection Tasks via Collaborative Smart Vehicles

Vehicular cloud computing (VCC) has been widely utilized to enhance traffic management and road safety both in the static and dynamic scenarios. In VCC, the onboard resources of involved smart vehicles (SVs) would be integrated as a resource pool and collaboratively accommodate various computation tasks. In this system, SVs in a section of the road can be grouped as a vehicular cloud to complete the detection job, e.g., collecting and preprocessing the road data. Then, an SV will be selected to aggregate all SVs’ detection results in the road section and upload the results to the remote monitoring platform via base stations (BSs). However, due to the high dynamics of vehicular mobility and the poor coverage of BSs in some areas, this vision is challenging to achieve. In this article, we propose a collaborative road detection system and schedule the detection task to different SVs in the road section. Specifically, 1) we model the collaborative detection process and formulate it as a task scheduling problem to minimize the response time, which is NP-hard. An adaptive location-aware scheduling scheme is proposed for task scheduling and 2) as there are road sections without coverage of any BS, therefore we have to utilize the SVs on the opposite lane to transmit the aggregation result in these road sections. Accordingly, an uploading strategy is proposed to decide to upload the feedback through SVs on the opposite lane or in the coverage area of the next BS. Extensive experiments show that our scheme can significantly reduce the response time overall and is close to the optimal solution.

Open-Source Data-Driven Cross-Domain Road Detection From Very High Resolution Remote Sensing Imagery.

High-precision road detection from very high resolution (VHR) remote sensing images has broad application value. However, the most advanced deep learning based methods often fail to identify roads when there is a distribution discrepancy between the training samples and test samples, due to their limited generalization ability. In this paper, to address this problem, an open-source data-driven domain-specific representation (OSM-DOER) framework is proposed for cross-domain road detection. On the one hand, as the spatial structure information of the source and target domains is similar, but the texture information is different, the domain-specific representation (DOER) framework is proposed, which not only aligns the distributions of the spatial structure information, but also learns the domain-specific texture information. Furthermore, in order to enhance the representation of the target domain data distribution, open-source and freely available OpenStreetMap (OSM) road centerline data are utilized to generate target domain samples, which are then used in the network training as the supervised information for the target domain. Finally, to verify the superiority of the proposed OSM-DOER framework, we conducted extensive experiments with the public SpaceNet and DeepGlobe road datasets, and large-scale road datasets from Birmingham in the UK and Shanghai in China. The experimental results demonstrate that the proposed OSM-DOER framework shows obvious advantages over the mainstream road detection methods, and the use of OSM road centerline data has great potential for the road detection task.

TOWARDS FINE-GRAINED ROAD MAPS EXTRACTION USING SENTINEL-2 IMAGERY

Abstract. Nowadays, it is highly important to keep road maps up-to-date since a great deal of services rely on them. However, to date, these labours have demanded a great deal of human attention due to their complexity. In the last decade, promising attempts have been carried out to fully-automatize the extraction of road networks from remote sensing imagery. Nevertheless, the vast majority of methods rely on aerial imagery (< 1 m), whose costs are not yet affordable for maintaining up-to-date maps. This work proves that it is also possible to accurately detect roads using high resolution satellite imagery (10 m). Accordingly, we have relied on Sentinel-2 imagery considering its freely availability and the higher revisit times compared to aerial imagery. It must be taken into account that the lack of spatial resolution of this sensor drastically increases the difficulty of the road detection task, since the feasibility to detect a road depends on its width, which can reach sub-pixel size in Sentinel-2 imagery. For that purpose, a new deep learning architecture which combines semantic segmentation and super-resolution techniques is proposed. As a result, fine-grained road maps at 2.5 m are generated from Sentinel-2 imagery.

Road Extraction in SAR Images Using Ordinal Regression and Road-Topology Loss

The road extraction task is mainly composed of two subtasks, namely, road detection and road centerline extraction. As the road detection task and road centerline extraction task are strongly correlated, in this paper, we introduce a multitask learning framework to detect roads and extract road centerlines simultaneously. For the road centerline extraction problem, existing works rely either on regression-based methods, or classification-based methods. The regression-based methods suffer from slow convergence and unsatisfactory local solutions. The classification-based methods ignore the fact that the closer the pixel is to the centerline, the higher our tolerance for its misclassification. To overcome these problems, we first convert the road centerline extraction problem into the problem of discrete normalized distance label prediction, which can be resolved by training an ordinal regressor. For the road extraction task, most of the previous studies apply pixel-wise loss function, for example, Cross-Entropy loss, which is not sufficient, as the road has special topology characteristics such as connectivity. Therefore, we propose a road-topology loss function to improve the connectivity and completeness of the extracted road. The road-topology loss function has two key characteristics: (i) The road-topology loss function combines road detection prediction and road centerline extraction prediction to promote the two subtasks to each other by using the correlation between the two subtasks; (ii) The road-topology loss can emphatically penalize gaps that often appear in road detection results and spurious segments that easily appear in centerline extraction results. In this paper, we select the AdamW optimizer to minimize the road-topology loss. Since there is no public dataset, we build a road extraction dataset to evaluate our method. State-of-the-art semantic segmentation networks (LinkNet34, DLinkNet34, DeeplabV3plus) are used as baseline methods to compare with two kinds of method. The first kind of method modifies the baseline method by adding the road centerline extraction task branch based on ordinal regression. The second kind of method uses the road topology loss and has the same network architecture as the first kind of method. For the road detection task, the two kinds of methods improve the baseline methods by up to 3.51% and 11.98% in IoU metric on our test dataset, respectively. For the road centerline extraction task, the two kinds of methods improve the baseline methods by up to 8.22% and 10.9% in the Quality metric on our test dataset.

ABSSNet: Attention-Based Spatial Segmentation Network for Traffic Scene Understanding.

The location information of road and lane lines is the supremely important thing for the automatic drive and auxiliary drive. The detection accuracy of these two elements dramatically affects the reliability and practicality of the whole system. In real applications, the traffic scene can be very complicated, which makes it particularly challenging to obtain the precise location of road and lane lines. Commonly used deep learning-based object detection models perform pretty well on the lane line and road detection tasks, but they still encounter false detection and missing detection frequently. Besides, existing convolution neural network (CNN) structures only pay attention to the information flow between layers, while it cannot fully utilize the spatial information inside the layers. To address those problems, we propose an attention-based spatial segmentation network for traffic scene understanding. We use the convolutional attention module to improve the network's understanding capacity of spatial location distribution. Spatial CNN (SCNN) obtains through the information flow within one single convolutional layer and improves the spatial relationship modeling ability of the network. The experimental results demonstrate that this method effectively improves the neural network's application ability of the spatial information, thereby improving the effect of traffic scene understanding. Furthermore, a pixel-level road segmentation dataset called NWPU Road Dataset is built to help improve the process of traffic scene understanding.

Efficient Outdoor Video Semantic Segmentation Using Feedback-Based Fully Convolution Neural Network

In this article, we focus on efficient semantic segmentation problem from sequential two-dimensional images, in which all pixels are classified into certain classes for scene understanding. Such problem is challenging because it involves constraints of both spatial and temporal consistencies, which have large difficulties in explicitly determining such structural constraints. Traditionally, such a problem is tackled using structured prediction method, such as conditional random field (CRF). However, pure CRF method suffers from very high complexity in computing high-order potentials and slow performance during inference step, which is unsuitable for efficient video segmentation in real scenario. In this article, a novel feedback-based deep fully convolutional neural network (CNN) is proposed to inherently incorporate spatial context through appending output feedback mechanism. The proposed method has the following contributions: 1) spatial context in images are easily captured through iterative feedback refinement, without the expensive postprocess step such as CRF refinement; 2) easily integrated with generic deep CNN structure; and 3) the inference time is greatly reduced for efficient image segmentation. Compared to current state-of-the-art methods, our proposed method was shown to provide up to 14% better accuracy on semantic segmentation task in challenging Camvid and Cityscapes datasets, while taking up to relatively 980% shorter inference time. The proposed method also shows its effectiveness for real-time road detection task of autonomous driving.

Road detection using cycle-consistent adversarial networks

Today, road detection is still a challenging task in intelligent driving. With the continuous improvement of computer vision, many methods of deep learning are used for road detection because they can achieve image features at a deeper level and discover road areas from raw RGB data. However, the method to detect the road areas accurately needs to be improved. We present a method that can extract the road area features and complete road detection tasks. Our method mainly includes the following points: (1) to introduce the cycle-consistent adversarial network to extract the road area features in a picture and complete image to image conversion and (2) to complete road detection by adding a new model and to improve the accuracy of detection. The results of our method are evaluated by uploading to the Karlsruhe Institute of Technology and Toyota Technological Institute road detection benchmark and named it as “road detection cycle-consistent adversarial networks.” Our method achieves an overall max F -measure of 88.63% and precision of 91.35%. In addition to high precision, our method also has a good robustness. Meanwhile, the accuracy for narrow road areas needs to be optimized in the future.

Road Boundaries Detection based on Modified Occupancy Grid Map Using Millimeter-wave Radar

Road region detection is a hot spot research topic in autonomous driving field. It requires to give consideration to accuracy, efficiency as well as prime cost. In that, we choose millimeter-wave (MMW) Radar to fulfill road detection task, and put forward a novel method based on MMW which meets real-time requirement. In this paper, a dynamic and static obstacle distinction step is firstly conducted to estimate the dynamic obstacle interference on boundary detection. Then, we generate an occupancy grid map using modified Bayesian prediction to construct a 2D driving environment model based on static obstacles, while a clustering procedure is carried out to describe dynamic obstacles. Next, a Modified Random Sample Consensus (Modified RANSAC) algorithm is presented to estimate candidate road boundaries from static obstacle maps. Results of our experiments are presented and discussed at the end. Note that, all our experiments in this paper are run in real-time on an experimental UGV (unmanned ground vehicle) platform equipped with Continental ARS 408-21 radar.

A real-time road detection method based on reorganized lidar data.

Road Detection is a basic task in automated driving field, in which 3D lidar data is commonly used recently. In this paper, we propose to rearrange 3D lidar data into a new organized form to construct direct spatial relationship among point cloud, and put forward new features for real-time road detection tasks. Our model works based on two prerequisites: (1) Road regions are always flatter than non-road regions. (2) Light travels in straight lines in a uniform medium. Based on prerequisite 1, we put forward difference-between-lines feature, while ScanID density and obstacle radial map are generated based on prerequisite 2. According to our method, we construct an array of structures to store and reorganize 3D input firstly. Then, two novel features, difference-between-lines and ScanID density, are extracted, based on which we construct a consistency map and an obstacle map in Bird Eye View (BEV). Finally, the road region is extracted by fusing these two maps and refinement is used to polish up our outcome. We have carried out experiments on the public KITTI-Road benchmark, achieving one of the best performances among the lidar-based road detection methods. To further prove the efficiency of our method on unstructured road, the visual outcomes in rural areas are also proposed.

3-D LiDAR + Monocular Camera: An Inverse-Depth-Induced Fusion Framework for Urban Road Detection

Road detection is an important task in autonomous navigation systems. In this paper, we propose a road detection framework induced by the inverse depth of LiDAR's point cloud. This framework is a fusion of a 3-D LiDAR and a monocular camera, where the 3-D point cloud of LiDAR is projected onto the camera's image frame, to exploit both range and color information. For the same road detection task, we propose an inverse-depth aware fully convolutional neural network based on image information and a line scanning strategy based on an inverse-depth histogram of LiDAR's point cloud. Finally, a conditional random field fusion method integrates the two road detection results. Our method is evaluated on KITTI-Road benchmark. Experiments demonstrate that our method achieves the state-of-the-art performance in road detection among all the referable methods that have ever reported their results on the KITTI-Road benchmark.

Automatic Road Detection and Centerline Extraction via Cascaded End-to-End Convolutional Neural Network

Accurate road detection and centerline extraction from very high resolution (VHR) remote sensing imagery are of central importance in a wide range of applications. Due to the complex backgrounds and occlusions of trees and cars, most road detection methods bring in the heterogeneous segments; besides for the centerline extraction task, most current approaches fail to extract a wonderful centerline network that appears smooth, complete, as well as single-pixel width. To address the above-mentioned complex issues, we propose a novel deep model, i.e., a cascaded end-to-end convolutional neural network (CasNet), to simultaneously cope with the road detection and centerline extraction tasks. Specifically, CasNet consists of two networks. One aims at the road detection task, whose strong representation ability is well able to tackle the complex backgrounds and occlusions of trees and cars. The other is cascaded to the former one, making full use of the feature maps produced formerly, to obtain the good centerline extraction. Finally, a thinning algorithm is proposed to obtain smooth, complete, and single-pixel width road centerline network. Extensive experiments demonstrate that CasNet outperforms the state-of-the-art methods greatly in learning quality and learning speed. That is, CasNet exceeds the comparing methods by a large margin in quantitative performance, and it is nearly 25 times faster than the comparing methods. Moreover, as another contribution, a large and challenging road centerline data set for the VHR remote sensing image will be publicly available for further studies.