New Dark Area Sensitive Tone Mapping for Deep Learning Based Traffic Sign Recognition

Khan, Jameel Ahmed; Yeo, Donghoon; Shin, Hyunchul

doi:10.3390/s18113776

Cited by 15 publications

(6 citation statements)

References 17 publications

(27 reference statements)

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“…However, the model size of this method is larger and may be affected by resource limitations. Khan et al [17] focused on solving the problem of darker areas in complex environments and proposed an intelligent traffic sign recognition system with a lighting pre-processing function to enhance light in the darker areas of the image using brightness enhancement technology. Their approach yielded satisfactory results when tested on the GTSDB dataset.…”

Section: Overview Of Traffic Sign Detection Methodsmentioning

confidence: 99%

Application of Sustainable Blockchain Technology in the Internet of Vehicles: Innovation in Traffic Sign Detection Systems

Liu,

Qian,

Zhang

et al. 2023

Sustainability

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With the rapid development of the Internet of Vehicles (IoV), traffic sign detection plays an indispensable role in advancing autonomous driving and intelligent transportation. However, current road traffic sign detection technologies face challenges in terms of information privacy protection, model accuracy verification, and result sharing. To enhance system sustainability, this paper introduces blockchain technology. The decentralized, tamper-proof, and consensus-based features of blockchain ensure data privacy and security among vehicles while facilitating trustworthy validation of traffic sign detection algorithms and result sharing. Storing model training data on distributed nodes reduces the system computational resources, thereby lowering energy consumption and improving system stability, enhancing the sustainability of the model. This paper introduces an enhanced GGS-YOLO model, optimized based on YOLOv5. The model strengthens the feature extraction capability of the original network by introducing a coordinate attention mechanism and incorporates a BiFPN feature fusion network to enhance detection accuracy. Additionally, the newly designed GGS convolutional module not only improves accuracy but also makes the model more lightweight. The model achieves an enhanced detection accuracy rate of 85.6%, with a reduced parameter count of 0.34×107. In a bid to broaden its application scope, we integrate the model with blockchain technology for traffic sign detection in the IoV. This method demonstrates outstanding performance in traffic sign detection tasks within the IoV, confirming its feasibility and sustainability in practical applications.

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Section: Overview Of Traffic Sign Detection Methodsmentioning

confidence: 99%

Application of Sustainable Blockchain Technology in the Internet of Vehicles: Innovation in Traffic Sign Detection Systems

Liu,

Qian,

Zhang

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…Hou et al [19] adopted the 1 v 1 method for blocked traffic signs, based on the HOG feature of the block, and calculated the confidence level for classification. Khan et al [20] designed the dark area sensitive mapping (DASTM) technique to improve the detector's effect in low-light traffic sign recognition, reaching 100% in GTSDB.…”

Section: Classification Of Traffic Signsmentioning

confidence: 99%

Learning Region-Based Attention Network for Traffic Sign Recognition

Zhou

Zhan

2021

Sensors

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Traffic sign recognition in poor environments has always been a challenge in self-driving. Although a few works have achieved good results in the field of traffic sign recognition, there is currently a lack of traffic sign benchmarks containing many complex factors and a robust network. In this paper, we propose an ice environment traffic sign recognition benchmark (ITSRB) and detection benchmark (ITSDB), marked in the COCO2017 format. The benchmarks include 5806 images with 43,290 traffic sign instances with different climate, light, time, and occlusion conditions. Second, we tested the robustness of the Libra-RCNN and HRNetv2p on the ITSDB compared with Faster-RCNN. The Libra-RCNN performed well and proved that our ITSDB dataset did increase the challenge in this task. Third, we propose an attention network based on high-resolution traffic sign classification (PFANet), and conduct ablation research on the design parallel fusion attention module. Experiments show that our representation reached 93.57% accuracy in ITSRB, and performed as well as the newest and most effective networks in the German traffic sign recognition dataset (GTSRB).

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“…These conditions can lead to increase in false detections and reduce the effectiveness of a TSDR system. Using a hybrid shape-based detection and recognition method in such conditions can be very useful and may give more superior performance [146].…”

Section: Current Issues and Challengesmentioning

confidence: 99%

Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges

Wali

Abdullah

Hannan

et al. 2019

Sensors

124

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The automatic traffic sign detection and recognition (TSDR) system is very important research in the development of advanced driver assistance systems (ADAS). Investigations on vision-based TSDR have received substantial interest in the research community, which is mainly motivated by three factors, which are detection, tracking and classification. During the last decade, a substantial number of techniques have been reported for TSDR. This paper provides a comprehensive survey on traffic sign detection, tracking and classification. The details of algorithms, methods and their specifications on detection, tracking and classification are investigated and summarized in the tables along with the corresponding key references. A comparative study on each section has been provided to evaluate the TSDR data, performance metrics and their availability. Current issues and challenges of the existing technologies are illustrated with brief suggestions and a discussion on the progress of driver assistance system research in the future. This review will hopefully lead to increasing efforts towards the development of future vision-based TSDR system.

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New Dark Area Sensitive Tone Mapping for Deep Learning Based Traffic Sign Recognition

Cited by 15 publications

References 17 publications

Application of Sustainable Blockchain Technology in the Internet of Vehicles: Innovation in Traffic Sign Detection Systems

Application of Sustainable Blockchain Technology in the Internet of Vehicles: Innovation in Traffic Sign Detection Systems

Learning Region-Based Attention Network for Traffic Sign Recognition

Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges

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