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 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) [ABSTRACT FROM AUTHOR]