Real-time Traffic Light Detection Research Articles

TL-Detector: Lightweight Based Real-Time Traffic Light Detection Model for Intelligent Vehicles

With the leap-forward development of intelligent driving, traffic light detection with high accuracy and high speed is important for intelligent transportation systems to ensure safety. However, existing methods encounter difficulties in balancing the detection speed and accuracy. This paper aims to present a flexible and robust lightweight model TL (Traffic Light)-Detector for real-time detection of traffic light. The model is composed of three parts. An enhanced backbone network combined with G-module is proposed to generate abundant information and reduce computational load, and the coordinate attention mechanism is introduced to focus on location features thereby strengthening the feature extract ability. The lightweight neck promotes multi-scale feature information aggregation for feature fusion to build a lightweight feature fusion network. The lightweight detection head adopts anchor-free mechanism to eliminate the hyperparameters related to the anchor. The dataset TL2022 of traffic light is built based on real traffic sceneries. Experimental results show that TL-Detector has the best comprehensive performance. TL-Detector achieves a detection speed of 277 FPS and a precision of 73.24% with only 0.72 GFLOPs. The experiments on LaRA (La Route Automatisee) public traffic light dataset show the excellent generalization ability. It indicates that TL-Detector can effectively achieve accurate and real-time detection for traffic lights.

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

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.

Real Time Traffic Light Detection by Autonomous Vehicles using Artificial Neural Network Techniques

Autonomous vehicles are the reality of the future, they will open up the way for future advanced systems where computers are expected to take over the decision making of driving. These automobiles are capable of sensing their environment and moving with little or no human input. The main goal of this research is to detect traffic light in real-time for autonomous vehicles. Apart from taking decisions to navigate in the right manner the autonomous vehicles important task is to detect traffic lights, so that it can obey the traffic rules with sufficient precision. The work carried out in this research makes use of two Artificial Intelligence technique, these techniques are compared in accomplishing the task of traffic light detection in real time. The two models that are designed and implemented are Convolution neural network (CNN) and Deep Convolution Inverse Graphics Network (DCIGN). The results clearly show that DCIGN out performance CNN by 8%.

Parallelism Optimized Architecture on FPGA for Real-Time Traffic Light Detection

In this paper, a portable assistance system is designed to help the visually impaired to detect the traffic light. The designed system is realized on the basis of the AdaBoost algorithm, which is fast and robust in object detections. In order to accelerate the AdaBoost-based approach, a flexible parallel architecture is implemented on the field-programmable gate array (FPGA) platform. The architecture is designed utilizing the parallelism of computations in the AdaBoost-based detection. The computations of the window integral image are implemented in parallel, and the confidences of the weak classifiers are calculated in parallel. The parameters of the weak classifiers are trained by the AdaBoost algorithm with multi-layer features in the MATLAB software, and then are configured on the FPGA platform via the Vivado design suite before the detection process. The parallelism optimized architecture is implemented on an Artix-7 FPGA at 200 MHZ. Experiments show that it can detect the traffic light in videos with a rate of 30 frames per second (fps).

Deep Learning Technique for Real-time Traffic Light Detection by Automated Vehicles

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

A Review on Real-time Traffic Light Detection Methods

A Review on Real-time Traffic Light Detection Methods

Real-Time Traffic Light Recognition Based on C-HOG Features

This paper proposes a real-time traffic light detection and recognition algorithm that would allow for the recognition of traffic signals in intelligent vehicles. This algorithm is based on C-HOG features (Color and HOG features) and Support Vector Machine (SVM). The algorithm extracted red and green areas in the video accurately, and then screened the eligible area. Thereafter, the C-HOG features of all kinds of lights could be extracted. Finally, this work used SVM to build a classifier of corresponding category lights. This algorithm obtained accurate real-time information based on the judgment of the decision function. Furthermore, experimental results show that this algorithm demonstrated accuracy and good real-time performance.

Saliency Map Generation by the Convolutional Neural Network for Real-Time Traffic Light Detection Using Template Matching

A critical issue in autonomous vehicle navigation and advanced driver assistance systems (ADAS) is the accurate real-time detection of traffic lights. Typically, vision-based sensors are used to detect the traffic light. However, the detection of traffic lights using computer vision, image processing, and learning algorithms is not trivial. The challenges include appearance variations, illumination variations, and reduced appearance information in low illumination conditions. To address these challenges, we present a visual camera-based real-time traffic light detection algorithm, where we identify the spatially constrained region-of-interest in the image containing the traffic light. Given, the identified region-of-interest, we achieve high traffic light detection accuracy with few false positives, even in adverse environments. To perform robust traffic light detection in varying conditions with few false positives, the proposed algorithm consists of two steps, an offline saliency map generation and a real-time traffic light detection. In the offline step, a convolutional neural network, i.e., a deep learning framework, detects and recognizes the traffic lights in the image using region-of-interest information provided by an onboard GPS sensor. The detected traffic light information is then used to generate the saliency maps with a modified multidimensional density-based spatial clustering of applications with noise (M-DBSCAN) algorithm. The generated saliency maps are indexed using the vehicle GPS information. In the real-time step, traffic lights are detected by retrieving relevant saliency maps and performing template matching by using colour information. The proposed algorithm is validated with the datasets acquired in varying conditions and different countries, e.g., USA, Japan, and France. The experimental results report a high detection accuracy with negligible false positives under varied illumination conditions. More importantly, an average computational time of 10 ms/frame is achieved. A detailed parameter analysis is conducted and the observations are summarized and reported in this paper.

Robust real-time traffic light detection and distance estimation using a single camera

This paper presents a robust technique to detect traffic lights during both day and night conditions and estimate their distance. The traffic light detection is based initially on color properties. To enhance the color on the video sequences, the acquisition is adapted according to the luminosity of the pixels on the top of the image. A fuzzy clustering provides a better division of the traffic light colors. The traffic light color properties have been estimated from registered sequences including both colors from LED spot lights and from traditional light bulbs. The filters rules based on the traffic light aspect ratios as well as the tracking stage are used to decide whether the spots on the frames are likely to be traffic lights. Then, the distance between traffic lights and the autonomous vehicle is estimated by applying Bayesian filters to the traffic lights represented on the frames. The tests are validated with more than an hour in real urban scenarios during day and night. The paper shows that the developed advanced driver assistance system is able to detect the traffic lights with 99.4% of accuracy in the range of 10–115m. The utility of this system has been demonstrated during the Public ROad Urban Driverless car test in Italy in 2013.