Vision for looking at traffic lights: Issues, survey, and perspectives
This paper presents the challenges that researchers must overcome in traffic light
recognition (TLR) research and provides an overview of ongoing work. The aim is to …
recognition (TLR) research and provides an overview of ongoing work. The aim is to …
Deep CNN-based real-time traffic light detector for self-driving vehicles
Due to the unavailability of Vehicle-to-Infrastructure (V2I) communication in current
transportation systems, Traffic Light Detection (TLD) is still considered an important module …
transportation systems, Traffic Light Detection (TLD) is still considered an important module …
Evaluating state-of-the-art object detector on challenging traffic light data
MB Jensen, K Nasrollahi… - Proceedings of the …, 2017 - openaccess.thecvf.com
Traffic light detection (TLD) is a vital part of both intelligent vehicles and driving assistance
systems (DAS). hard to determine the exact performance of a given method. In this paper we …
systems (DAS). hard to determine the exact performance of a given method. In this paper we …
A survey of FPGA-based vision systems for autonomous cars
On the road to making self-driving cars a reality, academic and industrial researchers are
working hard to continue to increase safety while meeting technical and regulatory …
working hard to continue to increase safety while meeting technical and regulatory …
A hierarchical deep architecture and mini-batch selection method for joint traffic sign and light detection
Traffic light and sign detectors on autonomous cars are integral for road scene perception.
The literature is abundant with deep learning networks that detect either lights or signs, not …
The literature is abundant with deep learning networks that detect either lights or signs, not …
An improved traffic lights recognition algorithm for autonomous driving in complex scenarios
Z Li, Q Zeng, Y Liu, J Liu, L Li - International Journal of …, 2021 - journals.sagepub.com
Image recognition is susceptible to interference from the external environment. It is
challenging to accurately and reliably recognize traffic lights in all-time and all-weather …
challenging to accurately and reliably recognize traffic lights in all-time and all-weather …
Traffic light recognition exploiting map and localization at every stage
Traffic light recognition is being intensively researched for the purpose of reducing traffic
accidents at intersections and realizing autonomous driving. However, conventional vision …
accidents at intersections and realizing autonomous driving. However, conventional vision …
Towards real-time traffic sign and traffic light detection on embedded systems
O Jayasinghe, S Hemachandra… - 2022 IEEE Intelligent …, 2022 - ieeexplore.ieee.org
Recent work done on traffic sign and traffic light detection focus on improving detection
accuracy in complex scenarios, yet many fail to deliver real-time performance, specifically …
accuracy in complex scenarios, yet many fail to deliver real-time performance, specifically …
Hybrid strategy for traffic light detection by combining classical and self‐learning detectors
F Gao, C Wang - IET Intelligent Transport Systems, 2020 - Wiley Online Library
Detection of the traffic light is a key function of the automatic driving system for urban traffic.
Considering the characteristics of classical and self‐learning algorithms, a fusion logic is …
Considering the characteristics of classical and self‐learning algorithms, a fusion logic is …
Deep traffic light detection by overlaying synthetic context on arbitrary natural images
Deep neural networks come as an effective solution to many problems associated with
autonomous driving. By providing real image samples with traffic context to the network, the …
autonomous driving. By providing real image samples with traffic context to the network, the …