Hidden nodes of neural network: Useful application in traffic sign recognition

Ali, Nursabillilah Mohd; Karis, Mohd Safirin; Safei, Javad

doi:10.1109/icsima.2014.7047445

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…This technique is created based on the biological neural network [7]. Like the human need to be taught in order to be smart, ANN used the same concept as it has to be trained first.…”

Section: Discussionmentioning

confidence: 99%

Analysis of frontal face detection performance by using Artificial Neural Network (ANN) and Speed-Up Robust Features (SURF) technique

Ali¹,

Karis²,

Aziz³

et al. 2016

AIP Conference Proceedings

Self Cite

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“…This technique is created based on the biological neural network [7]. Like the human need to be taught in order to be smart, ANN used the same concept as it has to be trained first.…”

Section: Discussionmentioning

confidence: 99%

Analysis of frontal face detection performance by using Artificial Neural Network (ANN) and Speed-Up Robust Features (SURF) technique

Ali¹,

Karis²,

Aziz³

et al. 2016

AIP Conference Proceedings

Self Cite

View full text Add to dashboard Cite

“…However, necessary to keep the vehicle running at a low speed to ensure the accuracy of recognition. Literature [5] proposes a feedforward back propagation neural network technology which extends supervision to realize traffic identification. This method not only gives an appropriate combination of the number of hidden layers and the number of input nodes, but also trains the network to the best state in a short period of time.…”

Section: Introductionmentioning

confidence: 99%

Research on Small Target Detection in Driving Scenarios Based on Improved Yolo Network

Lin

Han

et al. 2020

IEEE Access

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The obtainment of road condition information during driving is extremely important for a driver. However, drivers usually cannot notice multiple information at the same time, which definitely increases certain safety risks. Considering this problem, this paper designs a road information collection plus alarm system based on artificial intelligence to monitor road information. The underlying core algorithm of this system adopts the YOLO v3 network with the best comprehensive detection performance in the endto-end network. We use this network's advantage of fast detection speed to optimize on its original basis, and propose to ''copy'' part of the backbone network to build an auxiliary network, which enhances its feature extraction capability. Further, we apply the attention mechanism to the feature information fusion of the auxiliary network and the backbone network, suppress the invalid information channel, and improve the network processing efficiency. Besides, the training part of the network is optimized, and the mAP (mean Average Precision) is improved by setting the scale that meets the target to be detected. Through the test, the average test accuracy of the optimized network model reaches 84.76%, and the real-time detection speed on the 2080Ti reaches 41FPS. Compared with the previous network, the detection accuracy increases by 5.43% after optimization.INDEX TERMS Convolutional neural network, residual network, target detection, YOLO v3.

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Traffic Sign Detection and Recognition: Review and Analysis

et al. 2015

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Over the years, traffic sign detection and recognition systems gives extra value to driver assistance when driving, leading to more user-friendly driving experience and much improved safety for passengers. As part of Advanced Driving Systems (ADAS) one can be benefitted by using this system especially with driving incapacities by alerting and aid them about the existence of traffic signs to minimized unwanted circumstances during driving such as fatigue, poor sight and adverse weather conditions. Though a various number of traffic sign detection systems have been revised in literature; the need of design with a robust algorithm still remains open for further research. This paper purposes to design a system capable of performing traffic sign detection while considering variations of challenges such as color illumination, computational difficulty and functional constraints existed. Traffic sign detection is divided into three main parts namely; Pre-processing, Color segmentation and Thresholding. The color segmentation method is vital as it presents a detailed investigation of vision based color spaces in this case RGB, HSV and CMYK considering varying illumination conditions under different environments. This paper further highlights possible improvements to the proposed approaches for traffic sign detection.

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Hidden nodes of neural network: Useful application in traffic sign recognition

Cited by 5 publications

References 4 publications

Analysis of frontal face detection performance by using Artificial Neural Network (ANN) and Speed-Up Robust Features (SURF) technique

Analysis of frontal face detection performance by using Artificial Neural Network (ANN) and Speed-Up Robust Features (SURF) technique

Research on Small Target Detection in Driving Scenarios Based on Improved Yolo Network

Traffic Sign Detection and Recognition: Review and Analysis

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