Identification and Acknowledgment of Programmed Traffic Sign Utilizing Profound Convolutional Neural Organization

Vigneshwaran, P.; Prasath, N.; Islabudeen, M.; Arun, A.; Sampath, A.

doi:10.32604/iasc.2023.028444

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…Besides, there exist many other nominate solutions for TSD such as applying the Single Shot Detection algorithm in the VGG-16 model [10], analyzing semantically on objects [11], building a structure of detection, refinement and classification [12], deploying a single CNN architecture [13], combining a detector and semantics segmentation to address traffic signs [14], applying models of You only look once (YOLO) [15,16] or YOLO4 [17] and improved Mask R-CNN model. The traffic signs detection can also be researched in other ways using deep learning [18][19][20][21][22][23][24].…”

Section: Literature Reviewmentioning

confidence: 99%

A Deep Learning Model of Traffic Signs in Panoramic Images Detection

Huynh¹,

Le²,

Arif³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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To pursue the ideal of a safe high-tech society in a time when traffic accidents are frequent, the traffic signs detection system has become one of the necessary topics in recent years and in the future. The ultimate goal of this research is to identify and classify the types of traffic signs in a panoramic image. To accomplish this goal, the paper proposes a new model for traffic sign detection based on the Convolutional Neural Network for comprehensive traffic sign classification and Mask Region-based Convolutional Neural Networks (R-CNN) implementation for identifying and extracting signs in panoramic images. Data augmentation and normalization of the images are also applied to assist in classifying better even if old traffic signs are degraded, and considerably minimize the rates of discovering the extra boxes. The proposed model is tested on both the testing dataset and the actual images and gets 94.5% of the correct signs recognition rate, the classification rate of those signs discovered was 99.41% and the rate of false signs was only around 0.11.

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Section: Literature Reviewmentioning

confidence: 99%

A Deep Learning Model of Traffic Signs in Panoramic Images Detection

Huynh¹,

Le²,

Arif³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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“…The above algorithms cannot meet the requirement of segmentation accuracy. Although neural network segmentation algorithm has high accuracy, it requires a large amount of data support, and is faced with issues such as large computation and complex model design [26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

FPNC Net: A hydrogenation catalyst image recognition algorithm based on deep learning

Hou,

Zhao,

Cui

et al. 2024

PLoS ONE

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The identification research of hydrogenation catalyst information has always been one of the most important businesses in the chemical industry. In order to aid researchers in efficiently screening high-performance catalyst carriers and tackle the pressing challenge at hand, it is imperative to find a solution for the intelligent recognition of hydrogenation catalyst images. To address the issue of low recognition accuracy caused by adhesion and stacking of hydrogenation catalysts, An image recognition algorithm of hydrogenation catalyst based on FPNC Net was proposed in this paper. In the present study, Resnet50 backbone network was used to extract the features, and spatially-separable convolution kernel was used to extract the multi-scale features of catalyst fringe. In addition, to effectively segment the adhesive regions of stripes, FPN (Feature Pyramid Network) is added to the backbone network for deep and shallow feature fusion. Introducing an attention module to adaptively adjust weights can effectively highlight the target features of the catalyst. The experimental results showed that the FPNC Net model achieved an accuracy of 94.2% and an AP value improvement of 19.37% compared to the original CenterNet model. The improved model demonstrates a significant enhancement in detection accuracy, indicating a high capability for detecting hydrogenation catalyst targets

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mentioning

confidence: 99%

FPNC Net: A Hydrogenation Catalyst Image Recognition Algorithm Based on Deep Learning

Hou,

Zhao,

Cui

et al. 2023

Preprint

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The identification research of hydrogenation catalyst information has always been one of the most important businesses in the chemical industry. In order to aid researchers in efficiently screening high-performance catalyst carriers and tackle the pressing challenge at hand, it is imperative to find a solution for the intelligent recognition of hydrogenation catalyst images. To address the issue of low recognition accuracy caused by adhesion and stacking of hydrogenation catalysts, an image recognition algorithm of hydrogenation catalyst based on FPNC Net was proposed in this paper. In the present study, Resnet50 backbone network was used to extract the features, and spatially-separable convolution kernel was used to extract the multi-scale features of catalyst fringe. In addition, to effectively segment the adhesive regions of stripes, FPN (Feature Pyramid Network) is added to the backbone network for deep and shallow feature fusion. Introducing an attention module to adaptively adjust weights can effectively highlight the target features of the catalyst. The experimental results showed that the FPNC Net model achieved an accuracy of 94.2% and an AP value improvement of 19.37% compared to the original Center-Net model. The improved model demonstrates a significant enhancement in detection accuracy, indicating a high capability for detecting hydrogenation catalyst targets.

show abstract

Identification and Acknowledgment of Programmed Traffic Sign Utilizing Profound Convolutional Neural Organization

Cited by 3 publications

References 21 publications

A Deep Learning Model of Traffic Signs in Panoramic Images Detection

A Deep Learning Model of Traffic Signs in Panoramic Images Detection

FPNC Net: A hydrogenation catalyst image recognition algorithm based on deep learning

FPNC Net: A Hydrogenation Catalyst Image Recognition Algorithm Based on Deep Learning

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