Traffic Lights Detection in adverse conditions using Convolutional Neural Networks

Symeonidis, George; Groumpos, Peter P.; Dermatas, Evangelos

doi:10.33107/ubt-ic.2018.75

Cited by 1 publication

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“…Under the influence of a complex external environment, vision-based traffic signal recognition will become extremely difficult [22], so it is necessary to integrate traditional machine learning, deep learning, prior maps, and multi-sensor data fusion technologies to meet the application requirements [23,24]. It is important to emphasize that multi-sensor fusion positioning-assisted ROI acquisition is a significant method to improve recognition accuracy [25].…”

Section: Introductionmentioning

confidence: 99%

An ROI Optimization Method Based on Dynamic Estimation Adjustment Model

Zeng

Liu³

et al. 2023

Remote Sensing

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An important research direction in the field of traffic light recognition of autonomous systems is to accurately obtain the region of interest (ROI) of the image through the multi-sensor assisted method. Dynamic evaluation of the performance of the multi-sensor (GNSS, IMU, and odometer) fusion positioning system to obtain the optimum size of the ROI is essential for further improvement of recognition accuracy. In this paper, we propose a dynamic estimation adjustment (DEA) model construction method to optimize the ROI. First, according to the residual variance of the integrated navigation system and the vehicle velocity, we divide the innovation into an approximate Gaussian fitting region (AGFR) and a Gaussian convergence region (GCR) and estimate them using variational Bayesian gated recurrent unit (VBGRU) networks and a Gaussian mixture model (GMM), respectively, to obtain the GNSS measurement uncertainty. Then, the relationship between the GNSS measurement uncertainty and the multi-sensor aided ROI acquisition error is deduced and analyzed in detail. Further, we build a dynamic estimation adjustment model to convert the innovation of the multi-sensor integrated navigation system into the optimal ROI size of the traffic lights online. Finally, we use the YOLOv4 model to detect and recognize the traffic lights in the ROI. Based on laboratory simulation and real road tests, we verify the performance of the DEA model. The experimental results show that the proposed algorithm is more suitable for the application of autonomous vehicles in complex urban road scenarios than the existing achievements.

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Section: Introductionmentioning

confidence: 99%