Vision-Based Pavement Marking Detection and Condition Assessment—A Case Study

Xu, Shuyuan; Wang, Jun; Wu, Peng; Shou, Wenchi; Wang, Xiangyu; Chen, Mengcheng

doi:10.3390/app11073152

Cited by 25 publications

(16 citation statements)

References 35 publications

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“…e traditional method of pavement sign detection is usually based on feature or model. Xu et al [18] used the corner detector of FAST to detect a group of interest points and used the positions and feature vectors of interest points extracted from all template images to construct the template dataset. Finally, the structure-matching algorithm was used to test whether the subset of matched interest point pairs formed road signs matching the road signs in the die plate image.…”

Section: Road Marking Detectionmentioning

confidence: 99%

Road Sign Recognition Method Based on Segmentation and Attention Mechanism

Chen

2022

Mobile Information Systems

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With the development of autonomous driving, low-cost visual perception solutions have become a current research hotspot. However, the performance of the pure visual scheme in unfriendly environments such as low light, rain and fog, and complex traffic scenes has a large room for improvement. Moreover, with the development and application of deep learning, the balance between the accuracy and real-time performance of deep learning models is a difficult problem for current research. Aiming at the problems of large differences in the target scale of pavement signs and the difficulty of balancing model accuracy and real-time performance, a ground semantic cognition method based on segmentation and attention mechanism is proposed. The lightweight semantic segmentation model ERFNet is used to realize the semantic segmentation of pavement signs and the instantiation of lane lines. When only lane line detection is required, the prediction branch of lane line existence is introduced based on the lightweight semantic segmentation model ERFNet to realize lane line instantiation cognition, solve the imbalance of positive and negative lane line detection samples, and obtain the final lane line detection result via postprocessing. Deep features were used to guide shallow layers to extract semantic features at high resolution, and the model performance was further optimized without increasing the inference cost.

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Section: Road Marking Detectionmentioning

confidence: 99%

Road Sign Recognition Method Based on Segmentation and Attention Mechanism

Chen

2022

Mobile Information Systems

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“…The peak frequency is denoted by , where ℎ is the sequence pos peak in a series of frequency data. The possibility can be calculated u tion (6), where is the range for data comparison.…”

Section: Cable Force Determination 231 Determination Of Fundamental Frequencymentioning

confidence: 99%

“…The possibility P n . can be calculated using Equation (6), where m is the range for data comparison.…”

Section: Cable Force Determination 231 Determination Of Fundamental Frequencymentioning

confidence: 99%

“…A critical review on their implementations on construction sites can be found in [5]. Moreover, computer vision techniques have been used to collect as-is inventory data of existing civil assets [6] and to facilitate as-is 3D reconstruction [7,8]. Furthermore, image-based methods for dynamic response measurement, defect detection and condition assessment on existing structures have been summarized and presented in [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

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Research on Non-Contact and Non-Fixed Cable Force Measurement Based on Smartphone

Wang

Chen

et al. 2021

Applied Sciences

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Stay cable is the major load-carrying element in cable-stayed bridges. The process of monitoring cable forces would be beneficial to ensure the safety of bridges. The conventional sensor-based approaches to measure stay cable forces is complicated in operation, time-consuming and relatively expensive. In order to confront these disadvantages, a lightweight measurement method using smartphone imagery was proposed in this paper. The video data acquisition process was first standardized by using a pre-designed target. Then, a novel algorithm to extract the vibration displacement of stay cables under complex condition was developed. An automatic correction algorithm was provided to further improve the displacement results. On top of that, a smartphone-based software for determining cable forces was developed and tested on a real-life bridge. The results showed a maximum error of 1.99% compared with the cable force obtained by using a dynamic tester. The developed software is proven to be feasible in real-life projects and can achieve high accuracy in cable force determination. At the same time, the proposed method does not require a fixed camera for measurement and is not limited by personnel experience and measurement time, facilitating real-time monitoring of multiple projects, multiple cable surfaces and multiple personnel in a visual vibration environment.

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“…A safe and robust autonomous forest road deterioration system relies on accurate perception of the environment. To be more specific, an autonomous deterioration needs to accurately detect decision, protrusion, rutting, potholes and other deteriorations in real time in order to make the right control decisions that ensure safety [1]. Applications such as autonomous or surveillance systems would play a big role in our forest management in the very near future.…”

Section: Introductionmentioning

confidence: 99%

Forest Roads Damage Detection Based on Objected Detection Deep Learning Algorithms

Heidari

Najafi

Borges

2022

Preprint

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Due to the high cost of identifying and evaluating forest roads and the subsequent issue of maintenance, forest management has always faced challenges in terms of scheduling and implementation costs. Currently, monitoring the condition of forest roads has reached a critical stage due to budget allocation problems and lack of adequate supervision and requires low-cost or cost-effective monitoring. Today, smartphones have been used on public roads to identify road deterioration due to benefits such as usability, cost, ease of access, and expected accuracy. The use of smartphones in forest road development by the proposed system is a distributed information system that converts data from enterprise mode to field mode by harvesting and assessing forest road conditions and image processing technologies. The technology proposed in this research allows different information YOLOv4-v5 with improvements to this version including mosaic data augmentation and automatic learning of enclosing frames. In this research, we applied a new hybrid YOLOv4-v5 to the dataset's general applicability. We assessment forest road dataset to run an experiment, smartphone images by various aspects of the smartphone images (SI) dataset which is specialized for detect forest road deterioration. To enhance YOLO's ability to detect damaged scenes by proposed a new technique that takes information into frames. We expanded the scope of the model by applying it to a new orientation estimation task. The main disadvantage is the provision of qualitative model information on forest road activity and the indication of potential deterioration.

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Vision-Based Pavement Marking Detection and Condition Assessment—A Case Study

Cited by 25 publications

References 35 publications

Road Sign Recognition Method Based on Segmentation and Attention Mechanism

Road Sign Recognition Method Based on Segmentation and Attention Mechanism

Research on Non-Contact and Non-Fixed Cable Force Measurement Based on Smartphone

Forest Roads Damage Detection Based on Objected Detection Deep Learning Algorithms

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