CLEAR: An Efficient Traffic Sign Recognition Technique for Cyber-Physical Transportation Systems

Rai, Meghna; Khosla, Bhawna; Dhawan, Yash; Kharotia, Himanshi; Kumar, Neeraj; Bandi, Ajay

doi:10.1109/ucc56403.2022.00072

Cited by 2 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A robust, fast, and real time TSD supports and relieves the drivers, where it helps in improving the driving comfort and safety. Rai et al [22] have incorporated the You Only Look Once (YOLO) v5 model with the CLEAR framework for effective TSD in adverse climatic conditions. The evaluation performed on the open image dataset showed the efficacy of the presented model over the existing models by means of recall and mean average precision.…”

Section: Related Workmentioning

confidence: 99%

CNN-CLFFA: Support Mobile Edge Computing in Transportation Cyber Physical System

Bhansali,

Patra,

Divakarachari

et al. 2024

IEEE Access

View full text Add to dashboard Cite

In the present scenario, the transportation Cyber Physical System (CPS) improves the reliability and efficiency of the transportation systems by enhancing the interactions between the physical and cyber systems. With the provision of better storage ability and enhanced computing, cloud computing extends transportation CPS in Mobile Edge Computing (MEC). By inspecting the existing literatures, the cloud computing cannot fulfill the requirements in transportation CPS like lower context-awareness and latency. For enhancing the context-awareness and reducing the latency in a realistic MEC environment, an efficient portable deep learning model: Convolutional Neural Network (CNN) with Chaotic Lévy Flight based Firefly Algorithm (CLFFA) is implemented in this article. In the CNN model, the CLFFA selects the appropriate hyper-parameters or reduces the redundant parameters that results in minimal model size and inference latency than the traditional CNN models. Additionally, the CNN-CLFFA model significantly outperformed the existing models by means of recall, accuracy, and precision on the benchmark datasets like German Traffic Sign Recognition Benchmark (GTSRB), MIOvision Traffic Camera Dataset (MIO-TCD) classification, and VCifar-100 datasets. The numerical analysis demonstrates that the CNN-CLFFA model obtained maximum accuracy of 99.02%, 99.11%, and 99.03% on the VCifar-100, MIO-TCD, and GTSRB-T datasets, which are superior to the traditional models.

show abstract

Section: Related Workmentioning

confidence: 99%

CNN-CLFFA: Support Mobile Edge Computing in Transportation Cyber Physical System

Bhansali,

Patra,

Divakarachari

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches

Tahir,

Zhang,

Asim

et al. 2024

Algorithms

View full text Add to dashboard Cite

Enhancing the environmental perception of autonomous vehicles (AVs) in intelligent transportation systems requires computer vision technology to be effective in detecting objects and obstacles, particularly in adverse weather conditions. Adverse weather circumstances present serious difficulties for object-detecting systems, which are essential to contemporary safety procedures, infrastructure for monitoring, and intelligent transportation. AVs primarily depend on image processing algorithms that utilize a wide range of onboard visual sensors for guidance and decisionmaking. Ensuring the consistent identification of critical elements such as vehicles, pedestrians, and road lanes, even in adverse weather, is a paramount objective. This paper not only provides a comprehensive review of the literature on object detection (OD) under adverse weather conditions but also delves into the ever-evolving realm of the architecture of AVs, challenges for automated vehicles in adverse weather, the basic structure of OD, and explores the landscape of traditional and deep learning (DL) approaches for OD within the realm of AVs. These approaches are essential for advancing the capabilities of AVs in recognizing and responding to objects in their surroundings. This paper further investigates previous research that has employed both traditional and DL methodologies for the detection of vehicles, pedestrians, and road lanes, effectively linking these approaches with the evolving field of AVs. Moreover, this paper offers an in-depth analysis of the datasets commonly employed in AV research, with a specific focus on the detection of key elements in various environmental conditions, and then summarizes the evaluation matrix. We expect that this review paper will help scholars to gain a better understanding of this area of research.

show abstract

CLEAR: An Efficient Traffic Sign Recognition Technique for Cyber-Physical Transportation Systems

Cited by 2 publications

References 6 publications

CNN-CLFFA: Support Mobile Edge Computing in Transportation Cyber Physical System

CNN-CLFFA: Support Mobile Edge Computing in Transportation Cyber Physical System

Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches

Contact Info

Product

Resources

About