DeLTR: A Deep Learning Based Approach to Traffic Light Recognition

Cai, Yiyang; Li, Chenghua; Wang, Sujuan; Cheng, Jian

doi:10.1007/978-3-030-34113-8_50

Cited by 5 publications

(1 citation statement)

References 12 publications

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“…Cai et al [43] proposed a two-stage approach, where the detection part consisted of the SSDLite with Mo-bileNetv2 [83], whereas classification was performed by a small three-layer network.…”

Section: B Multi-stage Approachesmentioning

confidence: 99%

German Traffic Sign Recognition Using Convolutional Neural Network

Santosh¹,

Kumar²,

Sandeep³

et al. 2022

2022 6th International Conference on Electronics, Communication and Aerospace Technology

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Real-time traffic light recognition is essential for autonomous driving. Yet, a cohesive overview of the underlying model architectures for this task is currently missing. In this work, we conduct a comprehensive survey and analysis of traffic light recognition methods that use convolutional neural networks (CNNs). We focus on two essential aspects: datasets and CNN architectures. Based on an underlying architecture, we cluster methods into three major groups: (1) modifications of generic object detectors which compensate for specific task characteristics, (2) multi-stage approaches involving both rulebased and CNN components, and (3) task-specific single-stage methods. We describe the most important works in each cluster, discuss the usage of the datasets, and identify research gaps.

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Section: B Multi-stage Approachesmentioning

confidence: 99%

German Traffic Sign Recognition Using Convolutional Neural Network

Santosh¹,

Kumar²,

Sandeep³

et al. 2022

2022 6th International Conference on Electronics, Communication and Aerospace Technology

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show abstract

Absolute size IoU loss for the bounding box regression of the object detection

et al. 2022

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A Remote Sensing and Airborne Edge-Computing Based Detection System for Pine Wilt Disease

Liu

Shen³

et al. 2021

IEEE Access

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The pine wilt disease (PWD) is one of the most dangerous and destructive diseases to coniferous forests. The rapid spread trend and strong destruction directly threaten the security of forests. The complex spread pattern and the hard labor process of diagnosis call for an effective way to detect the infected areas. In this paper, an airborne edge-computing and lightweight deep learning based system are designed for PWD detection by using imagery sensors. Unmanned aerial vehicle (UAV) is firstly utilized to realize a large-scale coverage of forests, which can substantially reduce the hard labor. Except for infected trees, a large number of irrelevant images are also acquired by the UAV, which will overload the burden of process and transmission. Then a lightweight improved YOLOv4-Tiny based method (named as YOLOv4-Tiny-3Layers) is proposed to filter these uninterested images by leveraging the computation capability of edge computing, which can realize a fast coarse-grained detection with a low missing rate. Finally, all the remaining images are transmitted to the ground workstation for the final fine-grained detection. Experimental results show that the proposed system can implement a fast detection with superior performance as compared to other methods, which helps to detect the infected pine trees in a quick manner.INDEX TERMS pine wilt disease; remote sensing; airborne edge computing; lightweight deep learning; two-stage detection

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DeLTR: A Deep Learning Based Approach to Traffic Light Recognition

Cited by 5 publications

References 12 publications

German Traffic Sign Recognition Using Convolutional Neural Network

German Traffic Sign Recognition Using Convolutional Neural Network

Absolute size IoU loss for the bounding box regression of the object detection

A Remote Sensing and Airborne Edge-Computing Based Detection System for Pine Wilt Disease

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