scholarly journals Visual Meterstick: Preceding Vehicle Ranging Using Monocular Vision Based on the Fitting Method

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1081
Author(s):  
Chaochao Meng ◽  
Hong Bao ◽  
Yan Ma ◽  
Xinkai Xu ◽  
Yuqing Li
Keyword(s):  
Vehicle Detection ◽  
Semantic Segmentation ◽  
Safe Driving ◽  
Collision Warning ◽  
Bounding Box ◽  
Fitting Method ◽  
Measurement Results ◽  
Ranging Error ◽  
Preceding Vehicle ◽  
Pixel Value

The gradual application of deep learning in the field of computer vision and image processing has made great breakthroughs. Applications such as object detection, recognition and image semantic segmentation have been improved. In this study, to measure the distance of the vehicle ahead, a preceding vehicle ranging system based on fitting method was designed. First obtaining an accurate bounding box frame in the vehicle detection, the Mask R-CNN (region-convolutional neural networks) algorithm was improved and tested in the BDD100K (Berkeley deep derive) asymmetry dataset. This method can shorten vehicle detection time by 33% without reducing the accuracy. Then, according to the pixel value of the bounding box in the image, the fitting method was applied to the vehicle monocular camera for ranging. Experimental results demonstrate that the method can measure the distance of the preceding vehicle effectively, with a ranging error of less than 10%. The accuracy of the measurement results meets the requirements of collision warning for safe driving.

scholarly journals Preceding Vehicle Detection Based on Optimized Faster R-CNN Algorithm

2021 ◽  
Vol 1802 (3) ◽  
pp. 032075
Author(s):  
Yongqing Wang ◽  
Guochen Cui ◽  
Shufeng Wang ◽  
Junyou Zhang
Keyword(s):  
Vehicle Detection ◽  
Preceding Vehicle

scholarly journals Preceding Vehicle Detection Using Faster R-CNN Based on Speed Classification Random Anchor and Q-Square Penalty Coefficient

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 1024 ◽  
Author(s):  
Cui ◽  
Wang ◽  
Wang ◽  
Liu ◽  
Yuan ◽  
...  
Keyword(s):  
Experimental Validation ◽  
Autonomous Vehicle ◽  
Vehicle Detection ◽  
Detection Accuracy ◽  
Vehicle Speed ◽  
Challenging Problem ◽  
Penalty Coefficient ◽  
Preceding Vehicle ◽  
Vehicle Technologies ◽  
Vehicle Size

At present, preceding vehicle detection remains a challenging problem for autonomous vehicle technologies. In recent years, deep learning has been shown to be successful for vehicle detection, such as the faster region with a convolutional neural network (Faster R-CNN). However, when the host vehicle speed increases or there is an occlusion in front, the performance of the Faster R-CNN algorithm usually degrades. To obtain better performance on preceding vehicle detection when the speed of the host vehicle changes, a speed classification random anchor (SCRA) method is proposed. The reasons for degraded detection accuracy when the host vehicle speed increases are analyzed, and the factor of vehicle speed is introduced to redesign the anchors. Redesigned anchors can adapt to changes of the preceding vehicle size rule when the host vehicle speed increases. Furthermore, to achieve better performance on occluded vehicles, a Q-square penalty coefficient (Q-SPC) method is proposed to optimize the Faster R-CNN algorithm. The experimental validation results show that compared with the Faster R-CNN algorithm, the SCRA and Q-SPC methods have certain significance for improving preceding vehicle detection accuracy.

scholarly journals EFN: Field-Based Object Detection for Aerial Images

2020 ◽  
Vol 12 (21) ◽  
pp. 3630
Author(s):  
Jin Liu ◽  
Haokun Zheng
Keyword(s):  
Object Detection ◽  
Semantic Segmentation ◽  
Natural Images ◽  
Aerial Images ◽  
Aerial Image ◽  
Data Sets ◽  
Data Set ◽  
Bounding Box ◽  
Good Score ◽  
Bounding Boxes

Object detection and recognition in aerial and remote sensing images has become a hot topic in the field of computer vision in recent years. As these images are usually taken from a bird’s-eye view, the targets often have different shapes and are densely arranged. Therefore, using an oriented bounding box to mark the target is a mainstream choice. However, this general method is designed based on horizontal box annotation, while the improved method for detecting an oriented bounding box has a high computational complexity. In this paper, we propose a method called ellipse field network (EFN) to organically integrate semantic segmentation and object detection. It predicts the probability distribution of the target and obtains accurate oriented bounding boxes through a post-processing step. We tested our method on the HRSC2016 and DOTA data sets, achieving mAP values of 0.863 and 0.701, respectively. At the same time, we also tested the performance of EFN on natural images and obtained a mAP of 84.7 in the VOC2012 data set. These extensive experiments demonstrate that EFN can achieve state-of-the-art results in aerial image tests and can obtain a good score when considering natural images.

New Algorithm of Response Curve for Fitting HDR Image

2019 ◽  
Vol 34 (01) ◽  
pp. 2054001 ◽  
Author(s):  
Shi Qiu ◽  
Xuemei Li ◽  
Yongdong Huang ◽  
Zhengzhou Li ◽  
Xun Chen ◽  
...  
Keyword(s):  
Response Curve ◽  
Image Sequences ◽  
Response Curves ◽  
Time Intervals ◽  
Optimal Response ◽  
Fitting Method ◽  
Fitting Algorithm ◽  
Texture Characteristics ◽  
Pixel Value ◽  
Hdr Image

Based on the process of generating HDR images from LDR image sequences with different light exposures in the same scene, a new fitting method of camera response curves is proposed to solve the problem that the boundary of the fitting algorithm of camera response curves will be blurred and it is difficult to determine and verify the accuracy of the fitting curves. The optimal response curve is fitted by increasing LDR images step by step through considering the pixel value and texture characteristics. In order to validate the fitting effect of curves, we compare the photographed images and the real images in different time intervals on the basis of HDR images and response curves. We use RGB and gray image experiments to compare the current mainstream algorithms and the accuracy of our proposed algorithm can reach 96%, which has robustness.

scholarly journals Driver Characteristics Oriented Autonomous Longitudinal Driving System in Car-Following Situation

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6376
Author(s):  
Haksu Kim ◽  
Kyunghan Min ◽  
Myoungho Sunwoo
Keyword(s):  
Driving Behavior ◽  
Cruise Control ◽  
Driver Assistance ◽  
Collision Warning ◽  
Driving System ◽  
Traffic Jam ◽  
Car Following ◽  
Preceding Vehicle ◽  
Planning Algorithm ◽  
Control Traffic

Advanced driver assistance system such as adaptive cruise control, traffic jam assistance, and collision warning has been developed to reduce the driving burden and increase driving comfort in the car-following situation. These systems provide automated longitudinal driving to ensure safety and driving performance to satisfy unspecified individuals. However, drivers can feel a sense of heterogeneity when autonomous longitudinal control is performed by a general speed planning algorithm. In order to solve heterogeneity, a speed planning algorithm that reflects individual driving behavior is required to guarantee harmony with the intention of the driver. In this paper, we proposed a personalized longitudinal driving system in a car-following situation, which mimics personal driving behavior. The system is structured by a multi-layer framework composed of a speed planner and driver parameter manager. The speed planner generates an optimal speed profile by parametric cost function and constraints that imply driver characteristics. Furthermore, driver parameters are determined by the driver parameter manager according to individual driving behavior based on real driving data. The proposed algorithm was validated through driving simulation. The results show that the proposed algorithm mimics the driving style of an actual driver while maintaining safety against collisions with the preceding vehicle.

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