Road Obstacle Detection Research Articles

Improved VIDAR and machine learning-based road obstacle detection method

Improved VIDAR and machine learning-based road obstacle detection method

Obstacle Detection Method Based on RSU and Vehicle Camera Fusion

Road obstacle detection is an important component of intelligent assisted driving technology. Existing obstacle detection methods ignore the important direction of generalized obstacle detection. This paper proposes an obstacle detection method based on the fusion of roadside units and vehicle mounted cameras and illustrates the feasibility of a combined monocular camera inertial measurement unit (IMU) and roadside unit (RSU) detection method. A generalized obstacle detection method based on vision IMU is combined with a roadside unit obstacle detection method based on a background difference method to achieve generalized obstacle classification while reducing the spatial complexity of the detection area. In the generalized obstacle recognition stage, a VIDAR (Vision-IMU based identification and ranging) -based generalized obstacle recognition method is proposed. The problem of the low accuracy of obstacle information acquisition in the driving environment where generalized obstacles exist is solved. For generalized obstacles that cannot be detected by the roadside unit, VIDAR obstacle detection is performed on the target generalized obstacles through the vehicle terminal camera, and the detection result information is transmitted to the roadside device terminal through the UDP (User Data Protocol) protocol to achieve obstacle recognition and pseudo-obstacle removal, thereby reducing the error recognition rate of generalized obstacles. In this paper, pseudo-obstacles, obstacles with a certain height less than the maximum passing height of the vehicle, and obstacles with a height greater than the maximum passing height of the vehicle are defined as generalized obstacles. Pseudo-obstacles refer to non-height objects that appear to be “patches” on the imaging interface obtained by visual sensors and obstacles with a height less than the maximum passing height of the vehicle. VIDAR is a vision-IMU-based detection and ranging method. IMU is used to obtain the distance and pose of the camera movement, and through the inverse perspective transformation, it can calculate the height of the object in the image. The VIDAR-based obstacle detection method, the roadside unit-based obstacle detection method, YOLOv5 (You Only Look Once version 5), and the method proposed in this paper were applied to outdoor comparison experiments. The results show that the accuracy of the method is improved by 2.3%, 17.4%, and 1.8%, respectively, compared with the other four methods. Compared with the roadside unit obstacle detection method, the speed of obstacle detection is improved by 1.1%. The experimental results show that the method can expand the detection range of road vehicles based on the vehicle obstacle detection method and can quickly and effectively eliminate false obstacle information on the road.

Perspective Aware Road Obstacle Detection

While road obstacle detection techniques have become increasingly effective, they typically ignore the fact that, in practice, the apparent size of the obstacles decreases as their distance to the vehicle increases. In this letter, we account for this by computing a scale map encoding the apparent size of a hypothetical object at every image location. We then leverage this perspective map to (i) generate training data by injecting onto the road synthetic objects whose size corresponds to the perspective foreshortening; and (ii) incorporate perspective information in the decoding part of the detection network to guide the obstacle detector. Our results on standard benchmarks show that, together, these two strategies significantly boost the obstacle detection performance, allowing our approach to consistently outperform state-of-the-art methods in terms of instance-level obstacle detection.

Fast detection and ranging algorithm of blind track obstacles for embedded system application

Blind track scene has high complexity, and the existing target detection algorithm model is large, which cannot be directly applied to lightweight embedded platform, and it is easy to miss detection for small obstacles in long distance. To solve these problems, a real-time detection and ranging method of blind obstacles based on binocular vision is proposed. The method includes two stages: blind-track obstacle detection and blind-track obstacle ranging. In the obstacle detection stage, considering the requirements of embedded applications for model size and computing speed, a lightweight target detection network is designed. The network uses the deep separable convolution unit to extract the features of the blind road obstacles, and adds the channel attention module to filter and filter out high-quality feature information, so as to realize the fast detection of the blind road obstacles. To solve the problem of missed detection of long-distance small targets and obstacles, a multi-scale features fusion module is designed at the end of the network, which strengthens the detection ability of small targets of complex blind track scenes. In the obstacle ranging stage, an improved algorithm enhanced-speeded up robust features (ENH-SURF) based on binocular video is proposed to realize the real-time ranging of blind obstacles. Experimental results show that the detection accuracy of the detection algorithm is 83.2% in the blind road obstacle data set, and the evaluation indicators, such as model size and FPS, are better than the comparison algorithm. The distance measurement method is within range of 5 m, and the average relative error is 2.77%, which can meet the application needs of the blind.

An Obstacle Detection and Distance Measurement Method for Sloped Roads Based on VIDAR

Environmental perception systems can provide information on the environment around a vehicle, which is key to active vehicle safety systems. However, these systems underperform in cases of sloped roads. Real-time obstacle detection using monocular vision is a challenging problem in this situation. In this study, an obstacle detection and distance measurement method for sloped roads based on Vision-IMU based detection and range method (VIDAR) is proposed. First, the road images are collected and processed. Then, the road distance and slope information provided by a digital map is input into the VIDAR to detect and eliminate false obstacles (i.e., those for which no height can be calculated). The movement state of the obstacle is determined by tracking its lowest point. Finally, experimental analysis is carried out through simulation and real-vehicle experiments. The results show that the proposed method has higher detection accuracy than YOLO v5s in a sloped road environment and is not susceptible to interference from false obstacles. The most prominent contribution of this research work is to describe a sloped road obstacle detection method, which is capable of detecting all types of obstacles without prior knowledge to meet the needs of real-time and accurate detection of slope road obstacles.

Region of Interest Constrained Negative Obstacle Detection and Tracking With a Stereo Camera

Road object detection has been increasingly considered an essential task in automotive applications. Road objects are divided into two groups: i) above-ground objects such as vehicles and pedestrians and ii) road obstacles such as potholes and cracks. While above-ground object detection has been actively studied and achieved great advance, road obstacle detection has not been extensively studied. This paper proposes the road obstacle detection method based on 3D point cloud candidates extracted by height analysis. The 2D candidates of road obstacles are extracted from 3D candidates. A variant of RANSAC is applied to reduce point cloud matching errors in 3D reconstruction. The proposed system is implemented on the Jetson Xavier NX platform with a 20Hz camera, providing an accurate detection rate with feature tracking and visual inertial odometry in real-time. The proposed method is evaluated on a database consisting of concrete and tar-roads scenes. The experimental results verify the superiority of the proposed method in detecting road obstacles over the state-of-the-art methods.

Development of Road Obstacle Detection Technology for Crawler Robots Using 2D-LiDAR

Development of Road Obstacle Detection Technology for Crawler Robots Using 2D-LiDAR

Road Obstacle Detection in Bad Weather Based on Deep Learning

With the progress of the scientific era, people’s way of travel has become more diverse, not like the ancient carriage travel, but now the car, bus, train, high-speed rail, aircraft and so on for people to choose more, but the emergence of science and technology is like a double-edged sword, the convenience of travel mode will lead to traffic congestion, especially in bad weather In this paper, the deep learning method is used as the basic prediction algorithm to realize the road obstacle detection in bad weather. Under the influence of bad weather, people cannot judge the obstacles clearly, and 87 traffic accidents have occurred in two and a half years, The average number of accidents per day is 1.2, which is higher than that of 1.1 accidents / day in normal weather. The average number of accidents increases by about 20%.

Road Obstacle Object Detection Based on Improved YOLO V4

Abstract In recent years, as one of the important technical tasks in the field of deep learning, object detection has broad prospects and applications in the field of road obstacle detection. However, in the real driving scene, there are many obstacles, serious occlusion, overlap and other problems, so that the existing obstacle detection algorithm can not effectively detect the obstacles on the road, so it can not guarantee the driving safety. In order to solve the above problems, this paper improves on the basis of Yolo V4 algorithm. Firstly, kmeans + + clustering is used to generate a priori box suitable for the data set to enhance the scale adaptability; Then, the ciou is used as the loss function of coordinate prediction to evaluate the coincidence degree of prediction frame and truth value frame more reasonably. Finally, a suitable target detection data set is constructed by preprocessing the public data set cityccaps. The experimental results show that the improved algorithm can achieve more than 90% accuracy for obstacles with large number of targets in the training set. Compared with the original Yolo V4, the average detection accuracy of the improved algorithm is improved by 2.03%.

Implementation of a Lightweight Semantic Segmentation Algorithm in Road Obstacle Detection.

Due to deep learning’s accurate cognition of the street environment, the convolutional neural network has achieved dramatic development in the application of street scenes. Considering the needs of autonomous driving and assisted driving, in a general way, computer vision technology is used to find obstacles to avoid collisions, which has made semantic segmentation a research priority in recent years. However, semantic segmentation has been constantly facing new challenges for quite a long time. Complex network depth information, large datasets, real-time requirements, etc., are typical problems that need to be solved urgently in the realization of autonomous driving technology. In order to address these problems, we propose an improved lightweight real-time semantic segmentation network, which is based on an efficient image cascading network (ICNet) architecture, using multi-scale branches and a cascaded feature fusion unit to extract rich multi-level features. In this paper, a spatial information network is designed to transmit more prior knowledge of spatial location and edge information. During the course of the training phase, we append an external loss function to enhance the learning process of the deep learning network system as well. This lightweight network can quickly perceive obstacles and detect roads in the drivable area from images to satisfy autonomous driving characteristics. The proposed model shows substantial performance on the Cityscapes dataset. With the premise of ensuring real-time performance, several sets of experimental comparisons illustrate that SP-ICNet enhances the accuracy of road obstacle detection and provides nearly ideal prediction outputs. Compared to the current popular semantic segmentation network, this study also demonstrates the effectiveness of our lightweight network for road obstacle detection in autonomous driving.

Passive vision road obstacle detection: a literature mapping

ABSTRACT Obstacle detection is one of the main tasks in intelligent vehicle navigation systems. Several research works focused on the use of passive vision (cameras) to accomplish this task have been published. In this paper we present a literature mapping of the state of the art in road obstacle detection using frontal images. This mapping is based upon a systematic literature review that took into consideration papers published between 2007 and 2019. We analyze approaches based upon methods such as image segmentation, stereo vision, optical flow and neural networks and classify them accordingly to their characteristics and detection targets, such as vehicles, pedestrians or obstacles in general. We also inspect if they are performing pavement defects detection, such as potholes, puddles or other types of damage. The detection of pavement problems is important for the reality of in-development countries, which in many cases do not present well-maintained roads and may represent a threat to vehicular navigation. With this mapping we can identify the current state in this research area and also discuss future steps.

자율주행을 위한 u-, v-시차 히스토그램과 위험지도 기반의 장애물 검출

자율주행을 위한 u-, v-시차 히스토그램과 위험지도 기반의 장애물 검출

Fast road obstacle detection method based on maximally stable extremal regions

Road obstacle detection is an important component of the advanced driver assistance system, and to improve the speed and accuracy of road obstacle detection method is a vital task. In this article, fast image region-matching method based on the maximally stable extremal regions method is proposed to improve the speed of image matching. The theoretical feasibility of detection method combining monocular camera with inertial measurement unit (IMU) is clarified. The fast road obstacle detection method based on maximally stable extremal regions combining fast image region-matching method based on maximally stable extremal regions and the vision-IMU-based obstacle detection method is proposed to bypass obstacle classification and to reduce time and space complexity for road environment perception. The AdaBoost cascade detector, the speeded-up robust features-based obstacle detection method, and the proposed method are used to detect obstacles in outdoor contrast tests. Test results show that the proposed method has higher accuracy, and the reason of high accuracy is analyzed. The processing time of AdaBoost cascade detector, speeded-up robust features-based obstacle detection method, and proposed method are compared, and the results show that the proposed method has faster processing speed, and the reason of faster processing speed is analyzed.

On Road Obstacle Detection:A Review

On Road Obstacles detection from moving camera is also come under object detection. Road obstacles are a source of serious accidents that have a simple influence on driver safety, traffic flow efficiency and damage of the vehicle. The obstacle detection technologies are increasingly popular choices for driver assistant system. Obstacles detection is essential to avoid such kind of the accidents. Determining obstacles is very difficult and also it becomes complicated because of various problems like existence of shadow, environmental variations or an unexpected act of any moving things (e.g., car overtaking, animal coming) and many others with stationary camera. A new process is presented for detecting obstacles from moving camera and moving objects which overcomes numerous limitations above stationary cameras and moving/stationary objects. Further, paper analyses latest research developments to spot obstacles for moving cameras and moving objects with discussion of key points and limitations of each approach. Given the importance of obstacle detection, the main measure of interest was to decrease the road accidents and driver's safety. Detection of obstacles with moving camera and moving objects is more robust and reliable than stationary cameras.

A Study on Computational Time Reduction of Road Obstacle Detection by Parallel Image Processor

Automatic road obstacle detection is one of the significant problem in Intelligent Transport Systems (ITS). Many studies have been conducted for this interesting problem by using on-vehicle cameras. However, those methods still needs a dozens ofmillisecondsfor image processing. To develop the quick obstacle avoidance devices for vehicles, further computational time reduction is expected. Furthermore, regarding the applications, compact hardware is also expected for implementation. Thus, we study on computational time reduction of the road obstacle detection by using a small-type parallel image processor. Here, computational time is reduced by developing an obstacle detection algorithm which is appropriated to parallel processing concept of that hardware. According to the experimental evaluation of the new proposal, we could limit computational time for eleven milliseconds with a good obstacle detection performance.

Road Obstacle Detection in Stereo Vision Based on UV-disparity

Road Obstacle Detection in Stereo Vision Based on UV-disparity

An Road Obstacle Detection Method Based on Monocular Measurement

Obstacle detection in the intelligent vehicle vision navigation system occupies a very important role. The studies for the obstacles detecting, especially Monocular Measurement from the computer vision, simplifying monocular vision system to camera projection model. Getting the conversion relation between image coordinate and the world coordinate system through the geometry derivation to establish the measurement model and achieve the obstacle measurement. The experiment proved that the error of this measurement model selected is within the acceptable range.

Study on the Lane Mark Identification Method in Intelligent Vehicle Navigation

The intelligent vehicle navigation based on machine vision is the important part to realize Intelligent Transportation System (ITS), and it includes road detection, obstacle detection and motion control. Combining with foreign and domestic latest research trends, in this article, we mainly study the typical method of road detection, and point out the research and development tendency of intelligent vehicle navigation of technology based on the machine vision.

Road obstacle detection and tracking by an active and intelligent sensing strategy

In this paper, we address the problem of road obstacle deletion. We propose a method based on an active and intelligent sensing strategy. A sensor composed of a range finder coupled with a (charge-coupled-device) CCD camera is used. This sensor is mounted in front of a vehicle. The basic idea is first to determine 2D visual targets in intensity images of the camera. Then the range finder will be used not only to confirm or reject the existence of the detected visual targets, but also to acquire 3D information of the confirmed visual targets. The central problem of this strategy is how to detect 2D visual targets from intensity images of a road scene. In our method, we consider line segments as significant features. We use the concept ofline segment of interest and the concept ofdominant line segment. With the help of the identified dominant line segments in an image, we can effectively ascertain 2D visual targets. Finally, we use the range finder to confirm or reject a 2D visual target. A confirmed visual target is temporally tracked with the help of the range finder.

Visual Tracking by a Multisensorial Approach

The problem of road obstacle detection and tracking is addressed. The multisensorial approach is based on the use of a mixed camera/3D-sensor. The 3D-sensor is a laser range finder equiped with two mirrors to control the laser beam direction. The multisensor is endowed with two faculties which make it a smart active sensor. They are the controlled perception and the visual servoing which allow a close collaboration between the 3D-world and the intensity world. Obstacle detection is deduced from 3D-image analysis. Tracking combines intensity image processing and visual servoing. Obstacle dynamical state is obtained by a Kalrnan filter.