R‐ICTS: Recognize the Indian cautionary traffic signs in real‐time using an optimized adaptive boosting cascade classifier and a convolutional neural network

Cautionary traffic signs are of immense significance to traffic safety. In this study, a robust and optimal real‐time approach to recognize the Indian cautionary traffic signs (ICTS) is proposed. ICTS are all triangles with a white backdrop, a red border, and a black pattern. A dataset of 34,000 rea...

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Bibliographic Details
Published in:Concurrency and computation 2022-05, Vol.34 (10), p.n/a
Main Authors: Satti, Satish Kumar, K, Suganya Devi, P, Srinivasan
Format: Article
Language:English
Subjects:
Artificial neural networks
Classifiers
Color imagery
Computer vision
convolutional neural networks
Datasets
Error analysis
Image acquisition
Image contrast
Image enhancement
Indian cautionary traffic signs
intelligent transport system
Machine learning
Object recognition
optimized adaptive boosting cascade classifier
Recall
Traffic control
Traffic signs
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Summary:Cautionary traffic signs are of immense significance to traffic safety. In this study, a robust and optimal real‐time approach to recognize the Indian cautionary traffic signs (ICTS) is proposed. ICTS are all triangles with a white backdrop, a red border, and a black pattern. A dataset of 34,000 real‐time images has been acquired under various environmental conditions and categorized into 40 distinct classes. Pre‐processing techniques are used to transform RGB images to gray‐scale images and enhance contrast in images for superior performance. To find the ICTS, an optimized adaptive boosting cascade classifier is used. To classify the specific category of signs found by the optimized adaptive boosting cascade classifier, an 11‐layer CNN model is built. Finally, using computer vision methods, this model is tested in real‐time. Evaluation metrics such as precision, recall, F1 measure, error rate, and mAP are expressed on the ICTS, GTSDB, LISA, STSD, and DITS‐based datasets to evaluate the proposed method and compare the results of predictions with other investigations. When compared to other state‐of‐the‐art objects detection models such as SSD, YOLOv3, and Faster RCNN, the proposed model outperformed them all, with a precision of 97.15%, recall rate of 96.74%, an error rate of 3.26%, f1‐score of 96.94%, and mAP@0.5IoU of 95.6%. (Dataset available: 10.21227/yy4h‐rc98)
ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.6796