Vehicle Distance Measurement Method of Two-Way Two-Lane Roads Based on Monocular Vision

Yang, Rong; Yu, Shuyuan; Qi-hong, Yao; Huang, Jingbiao; Ya, Fuming

doi:10.3390/app13063468

Cited by 5 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In binocular vision, the calibration process of the binocular camera actually determines the relationship between the position of each point in the physical world and the position of the pixel in the two-dimensional image [27]. According to this conversion process, the conversion relationship of any object in space from the world coordinate system to the pixel coordinate system can be obtained, as shown in Formula (9).…”

Section: Calibration Of Binocular Cameramentioning

confidence: 99%

Research on YOLOv5 Vehicle Detection and Positioning System Based on Binocular Vision

Zhang,

Gong,

Chen

2024

WEVJ

View full text Add to dashboard Cite

Vehicle detection and location is one of the key sensing tasks of automatic driving systems. Traditional detection methods are easily affected by illumination, occlusion and scale changes in complex scenes, which limits the accuracy and robustness of detection. In order to solve these problems, this paper proposes a vehicle detection and location method for YOLOv5(You Only Look Once version 5) based on binocular vision. Binocular vision uses two cameras to obtain images from different angles at the same time. By calculating the difference between the two images, more accurate depth information can be obtained. The YOLOv5 algorithm is improved by adding the CBAM attention mechanism and replacing the loss function to improve target detection. Combining these two techniques can achieve accurate detection and localization of vehicles in 3D space. The method utilizes the depth information of binocular images and the improved YOLOv5 target detection algorithm to achieve accurate detection and localization of vehicles in front. Experimental results show that the method has high accuracy and robustness for vehicle detection and localization tasks.

show abstract

Section: Calibration Of Binocular Cameramentioning

confidence: 99%

Research on YOLOv5 Vehicle Detection and Positioning System Based on Binocular Vision

Zhang,

Gong,

Chen

2024

WEVJ

View full text Add to dashboard Cite

show abstract

“…In contrast, monocular vision only requires a single camera, enabling implementation through the pinhole imaging principle, resulting in lower costs and convenient operation [18,19]. The commonly used methods for monocular vision positioning include the Perspective-n-Point (PNP) method [20], the 2 of 14 imaging model method [21], the data regression modeling method [22], and the geometric relationship method [23]. The imaging model method achieves positioning based on a similar triangle relationship and is typically utilized for forward vehicle positioning in the transportation field but is not suitable for directional shifts of water surface targets.…”

Section: Introductionmentioning

confidence: 99%

Monocular Vision Guidance for Unmanned Surface Vehicle Recovery

Li,

Xi,

Shi

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

The positioning error of the GPS method at close distances is relatively large, rendering it incapable of accurately guiding unmanned surface vehicles (USVs) back to the mother ship. Therefore, this study proposes a near-distance recovery method for USVs based on monocular vision. By deploying a monocular camera on the USV to identify artificial markers on the mother ship and subsequently leveraging the geometric relationships among these markers, precise distance and angle information can be extracted. This enables effective guidance for the USVs to return to the mother ship. The experimental results validate the effectiveness of this approach, with positioning distance errors of less than 40 mm within a 10 m range and positioning angle errors of less than 5 degrees within a range of ±60 degrees.

show abstract

“…The experiment's lateral inaccuracy was within 0.3 m, while its error within 60 m was within 10%. Yang [27] proposed a vehicle distance measurement method for two-way lanes with an average inaccuracy of 3.15% within 60 m by combining the YOLOv5 algorithm and the geometric relation method. By combining the YOLOv3 algorithm with binocular vision, Fu [28] developed an unmanned aerial vehicle (UAV) ranging method, and the experimental results demonstrate that the measured relative error may reach a minimum of 2.54%.…”

Section: Introductionmentioning

confidence: 99%

Visual Ranging Based on Object Detection Bounding Box Optimization

Shi,

Li,

Che

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

Faster and more accurate ranging can be achieved by combining the object detection technique based on deep learning with conventional visual ranging. However, changes in scene, uneven lighting, fuzzy object boundaries and other factors may result in a non-fit phenomenon between the detection bounding box and the object. The pixel spacing between the detection bounding box and the object can cause ranging errors. To reduce pixel spacing, increase the degree of fit between the object detection bounding box and the object, and improve ranging accuracy, an object detection bounding box optimization method is proposed. Two evaluation indicators, WOV and HOV, are also proposed to evaluate the results of bounding box optimization. The experimental results show that the pixel width of the bounding box is optimized by 1.19~19.24% and the pixel height is optimized by 0~12.14%. At the same time, the ranging experiments demonstrate that the optimization of the bounding box improves the ranging accuracy. In addition, few practical monocular range measurement techniques can also determine the distance to an object whose size is unknown. Therefore, a similar triangle ranging technique based on height difference is suggested to measure the distance to items of unknown size. A ranging experiment is carried out based on the optimization of the detecting bounding box, and the experimental results reveal that the ranging relative error within 6 m is between 0.7% and 2.47%, allowing for precise distance measurement.

show abstract

Vehicle Distance Measurement Method of Two-Way Two-Lane Roads Based on Monocular Vision

Cited by 5 publications

References 23 publications

Research on YOLOv5 Vehicle Detection and Positioning System Based on Binocular Vision

Research on YOLOv5 Vehicle Detection and Positioning System Based on Binocular Vision

Monocular Vision Guidance for Unmanned Surface Vehicle Recovery

Visual Ranging Based on Object Detection Bounding Box Optimization

Contact Info

Product

Resources

About