A practical approach for detection and classification of traffic signs using Convolutional Neural Networks

Aghdam, Hamed Habibi; Heravi, Elnaz Jahani; Puig, Domènec

doi:10.1016/j.robot.2016.07.003

Cited by 79 publications

(38 citation statements)

References 16 publications

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“…In addition, similarly in traffic light detection, learning based methods provide an opportunity to solve illumination changes and varying viewpoints. Examples of deep learning traffic sign detection approaches include multi-scale CNN [27], Support Vector Machines (SVMs) to classify into global classes coupled with a CNN to further classify to finer categorization [28], a CNN with diluted convolutions [29], and a CNN with a Generative Adversarial Network (GAN) that enhances small images [12]. Our approach is the first to merge traffic sign and traffic light detection without a large loss in performance in either one of the global class categorization.…”

Section: Traffic Sign Detectorsmentioning

confidence: 99%

A Hierarchical Deep Architecture and Mini-batch Selection Method for Joint Traffic Sign and Light Detection

Pon

Adrienko²,

Harakeh

et al. 2018

2018 15th Conference on Computer and Robot Vision (CRV)

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Traffic light and sign detectors on autonomous cars are integral for road scene perception. The literature is abundant with deep learning networks that detect either lights or signs, not both, which makes them unsuitable for reallife deployment due to the limited graphics processing unit (GPU) memory and power available on embedded systems. The root cause of this issue is that no public dataset contains both traffic light and sign labels, which leads to difficulties in developing a joint detection framework. We present a deep hierarchical architecture in conjunction with a mini-batch proposal selection mechanism that allows a network to detect both traffic lights and signs from training on separate traffic light and sign datasets. Our method solves the overlapping issue where instances from one dataset are not labelled in the other dataset. We are the first to present a network that performs joint detection on traffic lights and signs. We measure our network on the Tsinghua-Tencent 100K benchmark for traffic sign detection and the Bosch Small Traffic Lights benchmark for traffic light detection and show it outperforms the existing Bosch Small Traffic light state-of-the-art method. We focus on autonomous car deployment and show our network is more suitable than others because of its low memory footprint and real-time image processing time. Qualitative results can be viewed at https://youtu.be/ YmogPzBXOw.

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Section: Traffic Sign Detectorsmentioning

confidence: 99%

A Hierarchical Deep Architecture and Mini-batch Selection Method for Joint Traffic Sign and Light Detection

Pon

Adrienko²,

Harakeh

et al. 2018

2018 15th Conference on Computer and Robot Vision (CRV)

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“…According to [112], CNN models are the most widely used deep learning algorithms for traffic sign classification to date. Of the examples applied to traffic sign classification are committee CNN [113], multi-scale CNN [114], multi-column CNN [102], multi-task CNN [111,115], hinge-loss CNN [116], deep CNN [46,117], a CNN with diluted convolutions [118], a CNN with a generative adversarial network (GAN) [119], and a CNN with SVM [120]. Based on these studies, a simultaneous detection and classification can be achieved using deep learning-based methods.…”

Section: Traffic Sign Detection Tracking and Classification Methodsmentioning

confidence: 99%

Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges

Wali

Abdullah

Hannan

et al. 2019

Sensors

121

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The automatic traffic sign detection and recognition (TSDR) system is very important research in the development of advanced driver assistance systems (ADAS). Investigations on vision-based TSDR have received substantial interest in the research community, which is mainly motivated by three factors, which are detection, tracking and classification. During the last decade, a substantial number of techniques have been reported for TSDR. This paper provides a comprehensive survey on traffic sign detection, tracking and classification. The details of algorithms, methods and their specifications on detection, tracking and classification are investigated and summarized in the tables along with the corresponding key references. A comparative study on each section has been provided to evaluate the TSDR data, performance metrics and their availability. Current issues and challenges of the existing technologies are illustrated with brief suggestions and a discussion on the progress of driver assistance system research in the future. This review will hopefully lead to increasing efforts towards the development of future vision-based TSDR system.

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“…Dilated convolution retains more spatial structure information by increasing the receptive field. It achieves better performance in addressing imagery that needs global information or speech text that needs long sequence information such as for semantic segmentation [28,36], image super division reconstruction [37], object detection and classification [38]. The receptive field is enlarged without increasing the number of parameters and calculations.…”

Section: ) Multi-column Convolution Neural Networkmentioning

confidence: 99%

Automatic Fish Population Counting by Machine Vision and a Hybrid Deep Neural Network Model

Zhang

Yang

Wang

et al. 2020

Animals

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In intensive aquaculture, the number of fish in a shoal can provide valuable input for the development of intelligent production management systems. However, the traditional artificial sampling method is not only time consuming and laborious, but also may put pressure on the fish. To solve the above problems, this paper proposes an automatic fish counting method based on a hybrid neural network model to realize the real-time, accurate, objective, and lossless counting of fish population in far offshore salmon mariculture. A multi-column convolution neural network (MCNN) is used as the front end to capture the feature information of different receptive fields. Convolution kernels of different sizes are used to adapt to the changes in angle, shape, and size caused by the motion of fish. Simultaneously, a wider and deeper dilated convolution neural network (DCNN) is used as the back end to reduce the loss of spatial structure information during network transmission. Finally, a hybrid neural network model is constructed. The experimental results show that the counting accuracy of the proposed hybrid neural network model is up to 95.06%, and the Pearson correlation coefficient between the estimation and the ground truth is 0.99. Compared with CNN- and MCNN-based methods, the accuracy and other evaluation indices are also improved. Therefore, the proposed method can provide an essential reference for feeding and other breeding operations.

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A practical approach for detection and classification of traffic signs using Convolutional Neural Networks

Cited by 79 publications

References 16 publications

A Hierarchical Deep Architecture and Mini-batch Selection Method for Joint Traffic Sign and Light Detection

A Hierarchical Deep Architecture and Mini-batch Selection Method for Joint Traffic Sign and Light Detection

Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges

Automatic Fish Population Counting by Machine Vision and a Hybrid Deep Neural Network Model

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