Traffic Light Recognition Based on Binary Semantic Segmentation Network

Kim, Hyun-Koo; Yoo, Kook-Yeol; Park, Ju H.; Jung, Ho-Youl

doi:10.3390/s19071700

Cited by 16 publications

(8 citation statements)

References 56 publications

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“…Originally, the ED-FCN has been developed for semantic segmentation [ 6 , 7 , 8 , 15 , 19 , 20 , 21 ] and classification [ 3 ]. In the case of the modified ED-FCN, called UNET as shown in Figure 1 b, the feature maps of the encoder network are combined into the maps of the decoder network via concatenation for bio-medical image segmentation [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Color Image Generation from LiDAR Reflection Data by Using Selected Connection UNET

Kim

Yoo

Jung

2020

Sensors

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In this paper, a modified encoder-decoder structured fully convolutional network (ED-FCN) is proposed to generate the camera-like color image from the light detection and ranging (LiDAR) reflection image. Previously, we showed the possibility to generate a color image from a heterogeneous source using the asymmetric ED-FCN. In addition, modified ED-FCNs, i.e., UNET and selected connection UNET (SC-UNET), have been successfully applied to the biomedical image segmentation and concealed-object detection for military purposes, respectively. In this paper, we apply the SC-UNET to generate a color image from a heterogeneous image. Various connections between encoder and decoder are analyzed. The LiDAR reflection image has only 5.28% valid values, i.e., its data are extremely sparse. The severe sparseness of the reflection image limits the generation performance when the UNET is applied directly to this heterogeneous image generation. In this paper, we present a methodology of network connection in SC-UNET that considers the sparseness of each level in the encoder network and the similarity between the same levels of encoder and decoder networks. The simulation results show that the proposed SC-UNET with the connection between encoder and decoder at two lowest levels yields improvements of 3.87 dB and 0.17 in peak signal-to-noise ratio and structural similarity, respectively, over the conventional asymmetric ED-FCN. The methodology presented in this paper would be a powerful tool for generating data from heterogeneous sources.

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Section: Introductionmentioning

confidence: 99%

Color Image Generation from LiDAR Reflection Data by Using Selected Connection UNET

Kim

Yoo

Jung

2020

Sensors

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“…In the work by Kim et al [40], the detection stage is performed by a semantic segmentation network, which is then used to calculate BBoxes. This is motivated by its better performance on very small objects.…”

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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“…Then, the decoder is used to generate a refined feature map from the results of the encoder, to output the final classification results for the traffic lights. Kim et al [85] proposed a traffic light recognition method based on deep learning, which consists of a semantic segmentation network and a fully convolutional network. The semantic segmentation network is employed to detect traffic lights and the fully convolutional network is used for traffic light classification.…”

Section: Traffic Signs and Lights Recognitionmentioning

confidence: 99%

A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods

Chen

et al. 2020

Applied Sciences

111

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Self-driving cars are a hot research topic in science and technology, which has a great influence on social and economic development. Deep learning is one of the current key areas in the field of artificial intelligence research. It has been widely applied in image processing, natural language understanding, and so on. In recent years, more and more deep learning-based solutions have been presented in the field of self-driving cars and have achieved outstanding results. This paper presents a review of recent research on theories and applications of deep learning for self-driving cars. This survey provides a detailed explanation of the developments of self-driving cars and summarizes the applications of deep learning methods in the field of self-driving cars. Then the main problems in self-driving cars and their solutions based on deep learning methods are analyzed, such as obstacle detection, scene recognition, lane detection, navigation and path planning. In addition, the details of some representative approaches for self-driving cars using deep learning methods are summarized. Finally, the future challenges in the applications of deep learning for self-driving cars are given out.

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Traffic Light Recognition Based on Binary Semantic Segmentation Network

Cited by 16 publications

References 56 publications

Color Image Generation from LiDAR Reflection Data by Using Selected Connection UNET

Color Image Generation from LiDAR Reflection Data by Using Selected Connection UNET

German Traffic Sign Recognition Using Convolutional Neural Network

A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods

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