Image-based automatic traffic lights detection system for autonomous cars: a review

Gautam, Seema; Kumar, Anuj

doi:10.1007/s11042-023-14340-1

Cited by 15 publications

(4 citation statements)

References 74 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are various issues and challenges related to traffic light detection. 1) Illumination: Images containing traffic lights may contain different illumination due to various environmental factors [10]. Traffic lights also have variability in their own lighting, as sometimes the traffic lights themselves may be off or in different cases (green, yellow, red).…”

Section: A Traffic Light Detection and Classification Modelsmentioning

confidence: 99%

On Salience-Sensitive Sign Classification in Autonomous Vehicle Path Planning: Experimental Explorations with a Novel Dataset

Greer

Isa

Deo

et al. 2022

2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)

View full text Add to dashboard Cite

One of the most important tasks for ensuring safe autonomous driving systems is accurately detecting road traffic lights and accurately determining how they impact the driver's actions. In various real-world driving situations, a scene may have numerous traffic lights with varying levels of relevance to the driver, and thus, distinguishing and detecting the lights that are relevant to the driver and influence the driver's actions is a critical safety task. This paper proposes a traffic light detection model which focuses on this task by first defining salient lights as the lights that affect the driver's future decisions. We then use this salience property to construct the LAVA Salient Lights Dataset, the first US traffic light dataset with an annotated salience property. Subsequently, we train a Deformable DETR object detection transformer model using Salience-Sensitive Focal Loss to emphasize stronger performance on salient traffic lights, showing that a model trained with this loss function has stronger recall than one trained without.

show abstract

Section: A Traffic Light Detection and Classification Modelsmentioning

confidence: 99%

On Salience-Sensitive Sign Classification in Autonomous Vehicle Path Planning: Experimental Explorations with a Novel Dataset

Greer

Isa

Deo

et al. 2022

2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)

View full text Add to dashboard Cite

show abstract

“…Therefore, deep learning methods suffer the properties of data-driven and a great deal of data labeling. In practical vehicle identification problems, it is difficult to ensure a substantial and wide variety of objects [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Vision-Based On-Road Nighttime Vehicle Detection and Tracking Using Improved HOG Features

Zhang,

Xu,

Shen

et al. 2024

Sensors

View full text Add to dashboard Cite

The lack of discernible vehicle contour features in low-light conditions poses a formidable challenge for nighttime vehicle detection under hardware cost constraints. Addressing this issue, an enhanced histogram of oriented gradients (HOGs) approach is introduced to extract relevant vehicle features. Initially, vehicle lights are extracted using a combination of background illumination removal and a saliency model. Subsequently, these lights are integrated with a template-based approach to delineate regions containing potential vehicles. In the next step, the fusion of superpixel and HOG (S-HOG) features within these regions is performed, and the support vector machine (SVM) is employed for classification. A non-maximum suppression (NMS) method is applied to eliminate overlapping areas, incorporating the fusion of vertical histograms of symmetrical features of oriented gradients (V-HOGs). Finally, the Kalman filter is utilized for tracking candidate vehicles over time. Experimental results demonstrate a significant improvement in the accuracy of vehicle recognition in nighttime scenarios with the proposed method.

show abstract

“…Cameras enable object detection and semantic understanding, which are impossible with lidar. For instance, the data obtained through a camera can be utilized to identify traffic signs and gesticulations [11,12]. Radar can accurately obtain information on the range, direction, and velocity of an object [13,14], which are challenging to obtain with a camera.…”

Section: Introductionmentioning

confidence: 99%

A Robust Target Detection Algorithm Based on the Fusion of Frequency-Modulated Continuous Wave Radar and a Monocular Camera

Yang,

Wang,

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Decision-level information fusion methods using radar and vision usually suffer from low target matching success rates and imprecise multi-target detection accuracy. Therefore, a robust target detection algorithm based on the fusion of frequency-modulated continuous wave (FMCW) radar and a monocular camera is proposed to address these issues in this paper. Firstly, a lane detection algorithm is used to process the image to obtain lane information. Then, two-dimensional fast Fourier transform (2D-FFT), constant false alarm rate (CFAR), and density-based spatial clustering of applications with noise (DBSCAN) are used to process the radar data. Furthermore, the YOLOv5 algorithm is used to process the image. In addition, the lane lines are utilized to filter out the interference targets from outside lanes. Finally, multi-sensor information fusion is performed for targets in the same lane. Experiments show that the balanced score of the proposed algorithm can reach 0.98, which indicates that it has low false and missed detections. Additionally, the balanced score is almost unchanged in different environments, proving that the algorithm is robust.

show abstract

Image-based automatic traffic lights detection system for autonomous cars: a review

Cited by 15 publications

References 74 publications

On Salience-Sensitive Sign Classification in Autonomous Vehicle Path Planning: Experimental Explorations with a Novel Dataset

On Salience-Sensitive Sign Classification in Autonomous Vehicle Path Planning: Experimental Explorations with a Novel Dataset

Vision-Based On-Road Nighttime Vehicle Detection and Tracking Using Improved HOG Features

A Robust Target Detection Algorithm Based on the Fusion of Frequency-Modulated Continuous Wave Radar and a Monocular Camera

Contact Info

Product

Resources

About