Road Detection Method Research Articles

Proposal of a flood damage road detection method based on deep learning and elevation data

Identifying an inundation area after a flood event is essential for planning emergency rescue operations. In this study, we propose a method to automatically determine inundated road segments by floods using image recognition technology, a deep learning model, and elevation data. First, we develop a training model using aerial photographs captured during a flood event. Then, the model is applied to aerial photographs captured during another flood event. The model visualizes the inundation status of roads on a 100-m mesh-by-mesh basis using aerial photographs and integrating the information on whether the mesh includes targeted road segments. Our results showed that the F-score was higher, 89%–91%, when we targeted only road segments with 15 m or less. Moreover, visualizing in GIS facilitated the classification of inundated roads, even within the same 100-m mesh, which is a relevant finding that complements deep learning object detection.

Snow‐ or ice‐covered road detection in winter road surface conditions using deep neural networks

AbstractTraffic accidents occur frequently in cold and snow‐ or ice‐covered regions due to weather changes that occur during the winter season. To detect the snow‐ or ice‐covered roads in road surface conditions, road surface images captured using fixed‐point cameras installed along the route are sufficient. This paper proposes a snow‐ or ice‐covered road detection method that uses the deep convolutional autoencoding Gaussian mixture model (DCAGMM) with structural similarity (SSIM). The DCAGMM method, which is an unsupervised anomaly detection method, is unaffected by imbalance in the training data. In addition, the end‐to‐end convolutional neural network implemented in the DCAGMM enables the capture of the unique characteristics of the road surface images. Finally, by reconstructing the input images as normal images, the comparison of the input and reconstructed images enables identification of snow‐ or ice‐covered road areas without requiring pixel‐level annotations. Furthermore, the road surface images include complex characteristics for reconstruction, and the SSIM‐based reconstruction error allows us to preserve the image quality of the reconstructed image. Experimental results obtained on real‐world road surface images demonstrate the effectiveness of the proposed method.

Extraction of roads from satellite images using MATLAB

Satellite imagery is increasingly being used in geographic information and urban planning, with road detection being one of its important tasks. However, traditional road detection methods have some limitations, such as poor handling of sharp turns and intersections. Therefore, the development of automatic road tracking methods based on morphological principles is of significant importance. This study addresses the urgent need for efficient and accurate road detection in satellite images. By combining morphological principles and convolutional operations, the proposed method can accurately identify road networks in complex environments. The advancement of this automatic road tracking technology holds great potential for improving navigation systems and urban planning, greatly enhancing the efficiency and accuracy of road information extraction. Additionally, this technology has far-reaching implications for urban planning. By more accurately capturing road layouts, urban planners can develop more reasonable road construction plans, optimize traffic flow, and improve the quality of life for city residents.

Risk Factor Attitude Survey and Step of Road Detection Method About Wheelchair of Elderly Person

We propose a step-detection system to improve the safety of electric wheelchairs for the elderly. In automobile driving support systems, roads and other environments are being improved, and collision detection and avoidance for vehicles and other pedestrians are being studied and prioritized. However, in the driving environment of electric wheelchairs, it is important to deal with bumps and one-way slopes on the road. In this study, we conducted a survey on elderly people’s awareness of risk factors related to wheelchairs and categorized these risk factors. In addition, we confirmed the usefulness of the step-detecting device by conducting a driving experiment.

Zebra-crossing detection based on cascaded Hough transform principle and vanishing point characteristics

Abstract In this study, a zebra-crossing detection method based on cascaded Hough transform (CHT) and vanishing point (VP) characteristics is proposed. In this method, the principle of detecting straight lines in the parallel coordinate system is applied to zebra-crossing detection. Each edge point of the image obtained by edge detection is represented in the parallel coordinate system to find the VP. Using the VP coordinate as the judgment condition, those straight lines that do not pass through the VP but meet the straight-line condition are excluded to obtain the straight lines passing through both sides of the zebra crossing, and finally fit the edge points on the straight line, and get the zebra-crossing fitting line segment. Experiments show that CHT has obvious advantages in detection accuracy and speed compared with the Hough transform. At the same time, VPs can be used to eliminate interference segments, which provide support for the accuracy of zebra-crossing detection. This method can get zebra-crossing location information without using region of interest extraction, which also provides a reference method for road detection in some specific cases.

Proposal of a Disrupted Road Detection Method in a Tsunami Event Using Deep Learning and Spatial Data

Tsunamis generated by undersea earthquakes can cause severe damage. It is essential to quickly assess tsunami-damaged areas to take emergency measures. In this study, I employ deep learning and develop a model using aerial photographs and road segment data. I obtained data from the aerial photographs taken after the Great East Japan Earthquake; the deep learning model used was YOLOv5. The proposed method based on YOLOv5 can determine damaged roads from aerial pictures taken after a disaster. The feature of the proposed method is to use training data from images separated by a specific range and to distinguish the presence or absence of damage related to the tsunami. The results show that the proposed method is more accurate than a comparable traditional method, which is constructed by labeling and learning the damaged areas. The highest F1 score of the traditional method was 60~78%, while the highest F1 score of the proposed method was 72~83%. The traditional method could not detect locations where it is difficult to determine the damage status from aerial photographs, such as where houses are not completely damaged. However, the proposed method was able to detect them.

A Lightweight Road Detection Algorithm Based on Multiscale Convolutional Attention Network and Coupled Decoder Head

Automatic road detection from remote sensing images has always been a significant research topic. It is of great value to many practical applications. However, there are still some problems need to be solved. First of all, most of existing road detection methods are inefficient because of the sequential processing of the decoder head. Secondly, some existing methods are unable to detect occluded road areas effectively. For this reason, we focus on the speed and occlusion problems in road detection network, and propose a new lightweight road detection method based on multi-scale convolution attention network (MSCAN) and coupled decoder head, LRDNet. In particular, LRDNet adopt multi-scale convolution attention network with large receptive field for feature extraction to solve the occlusion problem, and decode the road surface, road edge and road centerline in a coupled way to improve the speed of road detection and ensure that the road surface detection results have fewer burrs at the road edge. We have performed several experiments on the RNBD dataset. Compared with some state-of-the-art methods, the experimental results prove the validity of the proposed LRDNet. The code will be released soon on the site of https://github.com/dyl96/LRDNet.

An End-to-End Multi-Task Learning Model for Drivable Road Detection via Edge Refinement and Geometric Deformation

This paper presents a road detection method for autonomous driving based on an end-to-end neural network model. Our method takes advantage of both the characteristics of road boundary and multi-task learning of a deep convolutional network. By reassigning the label and rebalancing the loss of road pixels, we focus on the learning of hard examples on the boundary to refine its performance. Then, a road geometric transformation-based data augmentation method is proposed to enable the network model to be robust under traffic scenes. Based on these two novel methods, a unified architecture consisting of a shared deep residual encoder network and multi-branch decoder sub-networks is integrated. It adopts road scene classification as a supervised learning task to realize road segmentation and scene classification simultaneously. Experiments demonstrate that the proposed method has achieved the highest MaxF value in most road scenes. Both qualitative and quantitative evaluations on the KITTI-Road benchmark demonstrate our superior performance.

Unstructured Road Detection Method Based on RGB Maximum Two-Dimensional Entropy and Fuzzy Entropy

To solve the problem that lane keeping function for automatic driving and vehicle assisted driving will not work reliably on unstructured road without lane lines or other guide markings, this article uses the characteristics of information entropy to generate the RGB entropy image to pre-segment the road region on unstructured road image. At the same time, the maximum two-dimensional entropy algorithm is introduced to achieve the joint segmentation using gray and neighborhood gray to effectively reduce the impact of interference on segmentation. After that, the fuzzy entropy algorithm is used to judge and determine the actual road boundary by combining the results of RGB and maximum two-dimensional entropy image. Finally, using the improved least square fitting quadratic curve model to build the road boundary. Our method could well and rapidly extract the lane from unstructured road image and fit out the lane line, which helps to achieve visual based lane keeping on unstructured road for autopilot and driver assistance system.

ARDformer: Agroforestry Road Detection for Autonomous Driving Using Hierarchical Transformer.

Road detection is a crucial part of the autonomous driving system, and semantic segmentation is used as the default method for this kind of task. However, the descriptive categories of agroforestry are not directly definable and constrain the semantic segmentation-based method for road detection. This paper proposes a novel road detection approach to overcome the problem mentioned above. Specifically, a novel two-stage method for road detection in an agroforestry environment, namely ARDformer. First, a transformer-based hierarchical feature aggregation network is used for semantic segmentation. After the segmentation network generates the scene mask, the edge extraction algorithm extracts the trail’s edge. It then calculates the periphery of the trail to surround the area where the trail and grass are located. The proposed method is tested on the public agroforestry dataset, and experimental results show that the intersection over union is approximately 0.82, which significantly outperforms the baseline. Moreover, ARDformer is also effective in a real agroforestry environment.

Road extraction in vague images on gray scale consistency and improved MSR and D-S evidence

Road detection on aerial and remote sensing vague images is a hard task. In this paper, an automatic road detection method for the vague images is proposed. The method firstly uses an improved MSR algorithm to enhance image, and it automatically takes different scales in different image regions, based on the image depths obtained by the dark channel prior algorithm. Then the enhanced image is roughly segmented on the principle of the local gray scale consistency, in that, an eight-neighborhood template is considered as a processing unit in which a threshold is utilized for all the neighboring pixels of the detecting pixel. Finally, the Dempster-Shafer (D-S) evidence for road features is applied to finalize road tracing in the binary image, where, the road features include length, width, aspect ratio and fullness rate, all the parameters are obtained in the least external rectangle of a road segment, and then the detected roads are regulated. In experiments, 300 vague road images were selected for testing, by comparing to several traditional algorithms and recent semantic methods, the testing results show that the new method is satisfactory, and the detection accuracy is up to 89%.

Richer U-Net: Learning More Details for Road Detection in Remote Sensing Images

Road detection in remote sensing images has been an important research topic in the past few decades. However, with complex backgrounds and occlusion of vehicles and trees, it is difficult for most road detection methods to obtain complete and accurate results. There will be a large number of error and omission detections in such complex scenes due to the poor utilization of detailed information. Therefore, in this article, we propose a novel road detection method called Richer U-Net, which alleviates this problem by designing two detail enhancement strategies. First, considering that convolution operation will cause the loss of detailed information in the feature map, an enhanced detail recovery structure (EDRS) is introduced to make full use of those lost information. It combines the output of each convolutional layer at the same level for the detail recovery of decoding network, leading to more accurate segmentation results. Second, an edge-focused loss function is proposed to guide the network to pay more attention to the road edge area. By adding an enhancement factor, the pixels closer to edge will contribute more loss. The corresponding experiments are conducted on two public datasets, and it can be shown that our method effectively improves final detection results.

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.

Road Detection via a Dual-Task Network Based on Cross-Layer Graph Fusion Modules

Road detection based on remote sensing images is of great significance to intelligent traffic management. The performances of the mainstream road detection methods are mainly determined by their extracted features, whose richness and robustness can be enhanced by fusing features of different types and cross-layer connections. However, the features in the existing mainstream model frameworks are often similar in the same layer by the single-task training, and the traditional cross-layer fusion ways are too simple to obtain an efficient effect, so more complex fusion ways besides concatenation and addition deserve to be explored. Aiming at the above defects, we propose a dual-task network (DTnet) for road detection and cross-layer graph fusion module (CGM): the DTnet consists of two parallel branches for road area and edge detection, respectively, while enhancing the feature diversity by fusing features between two branches through our designed feature bridge modules (FBM). The CGM improves the cross-layer fusion effect by a complex feature stream graph, and four graph patterns are evaluated. Experimental results on three public datasets demonstrate that our method effectively improves the final detection result.

Aerial Remote Sensing Image Cascaded Road Detection Network Based on Edge Sensing Module and Attention Module

Automatic road detection has always been an important research problem in the field of remote sensing image processing, which is of great significance for many applications. However, many road areas cannot be effectively detected and the existing road detection methods suffer from unsmooth edges due to shadow phenomena and the occlusion of objects such as trees. For this reason, we propose a cascaded automatic road detection network based on edge sensing module and attention module, called CasEANet. CasEANet consists of three tasks including road surface detection, road edge detection and road centerline extraction. An encoder-decoder structure integrating edge sensing module and attention module is applied to detect the road surface. In order to obtain smoother road edges, the edge sensing module is designed to enhance the perception of road edges. The attention module is employed to guide the network to reinforce the perception of global information, aiming to solve the problem of discontinuity in the detection of road surfaces that are obscured by trees. The centerline extraction is adopted to assist the training of road surface detection. Experiments performed on the RNBD dataset prove the effectiveness of CasEANet. Specially, the F1 score, overall accuracy and balanced error rates of the CasEANet are 0.946, 0.986 and 0.0219 respectively, outperform the other state-of-the-art methods. The code will release soon on https://github.com/HITLDY/CasEANet.

Road Detection Based on Edge Feature with GAC Model in Aerial Image

Road detection from aerial images still is a challenging task since it is heavily influenced by spectral reflectance, shadows and occlusions. In order to increase the road detection accuracy, a proposed method for road detection by GAC model with edge feature extraction and segmentation is studied in this paper. First, edge feature can be extracted using the proposed gradient magnitude with Canny operator. Then, a reconstructed gradient map is applied in watershed transformation method, which is segmented for the next initial contour. Last, with the combination of edge feature and initial contour, the boundary stopping function is applied in the GAC model. The road boundary result can be accomplished finally. Experimental results show, by comparing with other methods in [Formula: see text]-measure system, that the proposed method can achieve satisfying results.

Multi-Classifier Feature Fusion-Based Road Detection for Connected Autonomous Vehicles

Connected autonomous vehicles (CAVs) currently promise cooperation between vehicles, providing abundant and real-time information through wireless communication technologies. In this paper, a two-level fusion of classifiers (TLFC) approach is proposed by using deep learning classifiers to perform accurate road detection (RD). The proposed TLFC-RD approach improves the classification by considering four key strategies such as cross fold operation at input and pre-processing using superpixel generation, adequate features, multi-classifier feature fusion and a deep learning classifier. Specifically, the road is classified as drivable and non-drivable areas by designing the TLFC using the deep learning classifiers, and the detected information using the TLFC-RD is exchanged between the autonomous vehicles for the ease of driving on the road. The TLFC-RD is analyzed in terms of its accuracy, sensitivity or recall, specificity, precision, F1-measure and max F measure. The TLFC- RD method is also evaluated compared to three existing methods: U-Net with the Domain Adaptation Model (DAM), Two-Scale Fully Convolutional Network (TFCN) and a cooperative machine learning approach (i.e., TAAUWN). Experimental results show that the accuracy of the TLFC-RD method for the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) dataset is 99.12% higher than its competitors.

Urban road mapping based on an end-to-end road vectorization mapping network framework

Reliable urban road vector maps are essential for urban analysis because the spatial distribution of road networks reflects urban development under the combined effects of nature and socio-economics. Diverse very high resolution (VHR) remote sensing images are now available, enabling explicit extraction of urban road vector maps over wide areas. Urban road vectorization mapping consists of two separate tasks: road extraction and road vectorization. The traditional methods focus on the road extraction task, and can obtain a good performance when using a pixel-based metric. However, the road vectorization methods are faced with the problem of road connectivity. In this work, to implement urban road vectorization mapping in a unified way, an end-to-end road vectorization mapping network (RVMNet) framework is proposed. The proposed RVMNet framework consists of a node proposal network (NPN) module and a node connectivity based road refinement module. In the NPN module, a fully convolutional network is adopted for the road node extraction. This improves the connectivity of the road mask by providing supervised information in the form of the road nodes, which are actually part of the road mask. The road mask is then converted into a road vector map by vectorization. In the node connectivity based road refinement module, road nodes are inserted into the road vector map to improve the connectivity. We compared RVMNet with the other state-of-the-art road detection methods on two public road datasets (SpaceNet 3 and DeepGlobe). The results of this comparison showed that combining road extraction and road vectorization into a unified framework is an efficient and accurate strategy for urban road vectorization mapping because it can propose road nodes that help to improve the road connectivity. Moreover, we constructed the novel UrbanRoadNet dataset, covering six cities (Beijing city center; Helsinki; Wuhan; Macao; the Wan Chai area of Hong Kong; and Shanghai). We then applied the RVMNet framework to the data from the six cities, obtaining an improvement in the vector-based average path length similarity (APLS) value of 4.1%. The spatial transfer assessments from both the qualitative and quantitative aspects corroborated the robust generalizability of the proposed method, and further verified the effectiveness of the proposed approach for large-scale road vectorization mapping at a very high resolution. It was also found that road vector spatial distribution is a useful way to reflect urban development.

RLSNAKE: A HYBRID REINFORCEMENT LEARNING APPROACH FOR ROAD DETECTION

Abstract. Road network detection from very high resolution satellite and aerial images is highly important for diverse domains. Although an expert can label road pixels in a given image, this operation is prone to error and quite time consuming remembering that road maps must be updated regularly. Therefore, various computer vision based automated algorithms have been proposed in the last two decades. Nevertheless, due to the diversity of scenes, the field is still open for robust methods which might detect roads on different resolution images of different type of environments. In this study, we picked an earlier proposed road detection method which works based on traditional computer vision and probability theory algorithms. We improved it by further steps using reinforcement learning theory. With the help of the novel hybrid technique (traditional computer vision method combined with reinforcement learning based artificial intelligence), we achieved a solution that we call RLSnake. This new method can learn new image scenes and resolutions rapidly and can work reliably. We believe that the proposed RLSnake will be a significant step in the remote sensing field in order to develop solutions which might increase performance by combining the power of traditional and new techniques.

Detection and Modeling of Unstructured Roads in Forest Areas Based on Visual-2D Lidar Data Fusion

The detection and recognition of unstructured roads in forest environments are critical for smart forestry technology. Forest roads lack effective reference objects and manual signs and have high degrees of nonlinearity and uncertainty, which pose severe challenges to forest engineering vehicles. This research aims to improve the automation and intelligence of forestry engineering and proposes an unstructured road detection and recognition method based on a combination of image processing and 2D lidar detection. This method uses the “improved SEEDS + Support Vector Machine (SVM)” strategy to quickly classify and recognize the road area in the image. Combined with the remapping of 2D lidar point cloud data on the image, the actual navigation requirements of forest unmanned navigation vehicles were fully considered, and road model construction based on the vehicle coordinate system was achieved. The algorithm was transplanted to a self-built intelligent navigation platform to verify its feasibility and effectiveness. The experimental results show that under low-speed conditions, the system can meet the real-time requirements of processing data at an average of 10 frames/s. For the centerline of the road model, the matching error between the image and lidar is no more than 0.119 m. The algorithm can provide effective support for the identification of unstructured roads in forest areas. This technology has important application value for forestry engineering vehicles in autonomous inspection and spraying, nursery stock harvesting, skidding, and transportation.