Traffic Lights Detection and Recognition Algorithm Based on Multi-feature Fusion

Wu, Yuan; Geng, Keke; Xue, Peiling; Yin, Guodong; Zhang, Ning; Lin, Yan

doi:10.1109/icivc47709.2019.8980828

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…However, transfer learning can be used to reduce the computational and time resources during training [ 24 , 25 ]. In some cases, high recognition accuracy is achieved by fusing HOG features and features extracted by CNN [ 9 ]. Network models such as RetinaNet [ 26 ], and YOLO [ 8 , 22 ] have also been studied.…”

Section: Related Workmentioning

confidence: 99%

“…The learning-based approach requires many traffic light images to construct a neural net that detects the traffic light. Because of the rapid development of machine learning techniques, this is currently one of the most popular approaches [ 8 , 9 ]. Some leaning based methods include not only traffic light detection but also car detection [ 8 ] and approaches that recognize which lane a traffic light belongs to have been developed [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Traffic Light Detection with Frequency Patterns Using a High-Speed Camera

Yabuuchi

Hirano

Senoo

et al. 2020

Sensors

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LEDs are widely employed as traffic lights. Because most LED traffic lights are driven by alternative power, they blink at high frequencies, even at twice their frequencies. We propose a method to detect a traffic light from images captured by a high-speed camera that can recognize a blinking traffic light. This technique is robust under various illuminations because it can detect traffic lights by extracting information from the blinking pixels at a specific frequency. The method is composed of six modules, which includes a band-pass filter and a Kalman filter. All the modules run simultaneously to achieve real-time processing and can run at 500 fps for images with a resolution of 800 × 600. This technique was verified on an original dataset captured by a high-speed camera under different illumination conditions such as a sunset or night scene. The recall and accuracy justify the generalization of the proposed detection system. In particular, it can detect traffic lights with a different appearance without tuning parameters and without datasets having to be learned.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Real-Time Traffic Light Detection with Frequency Patterns Using a High-Speed Camera

Yabuuchi

Hirano

Senoo

et al. 2020

Sensors

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show abstract

“…28 On one hand, combining traditional feature extraction and recognition methods with deep learning can make full use of their complementarity to improve system performance. 29 On the other hand, integrating real-time positioning and prior map data to obtain ROI and reduce environmental interference is helpful to improve the performance of the traffic lights recognition algorithm. 30 Accurate and reliable traffic lights recognition at alltime and all-weather is essential to the safety of autonomous vehicle systems.…”

Section: Introductionmentioning

confidence: 99%

An improved traffic lights recognition algorithm for autonomous driving in complex scenarios

Zeng

Liu³

et al. 2021

International Journal of Distributed Sensor Networks

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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 conditions. This article proposed an improved vision-based traffic lights recognition algorithm for autonomous driving, integrating deep learning and multi-sensor data fusion assist (MSDA). We introduce a method to obtain the best size of the region of interest (ROI) dynamically, including four aspects. First, based on multi-sensor data (RTK BDS/GPS, IMU, camera, and LiDAR) acquired in a normal environment, we generated a prior map that contained sufficient traffic lights information. And then, by analyzing the relationship between the error of the sensors and the optimal size of ROI, the adaptively dynamic adjustment (ADA) model was built. Furthermore, according to the multi-sensor data fusion positioning and ADA model, the optimal ROI can be obtained to predict the location of traffic lights. Finally, YOLOv4 is employed to extract and identify the image features. We evaluated our algorithm using a public data set and actual city road test at night. The experimental results demonstrate that the proposed algorithm has a relatively high accuracy rate in complex scenarios and can promote the engineering application of autonomous driving technology.

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Yolov7-based Brake Light Detection Model for Avoiding Rear-End Collisions

Kushwaha,

Abirami

2023

2023 12th International Conference on Advanced Computing (ICoAC)

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Traffic Lights Detection and Recognition Algorithm Based on Multi-feature Fusion

Cited by 4 publications

References 12 publications

Real-Time Traffic Light Detection with Frequency Patterns Using a High-Speed Camera

Real-Time Traffic Light Detection with Frequency Patterns Using a High-Speed Camera

An improved traffic lights recognition algorithm for autonomous driving in complex scenarios

Yolov7-based Brake Light Detection Model for Avoiding Rear-End Collisions

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