A Novel Approach for Detection and Recognition of Traffic Signs for Automatic Driver Assistance System Under Cluttered Background

Manjunatha, Hemanth; Danti, Ajit; Arunkumar, K.

doi:10.1007/978-981-13-9181-1_36

Cited by 10 publications

(1 citation statement)

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“…This technique improves the accuracy of the detection. For picture pre-processing, this article primarily employs image enhancement and color space conversion [19].…”

Section: Pre-processing Stagementioning

confidence: 99%

Detection and Recognition of Traffic Sign Boards using Random Forest Classifier

Jagadeesh¹,

Sree²

2022

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The traffic sign recognition system is a vital aspect of an intelligent transportation system since it provides information to drivers to help them drive more safely and effectively. This paper addresses some of these concerns, which will be accomplished in two steps. The first is the detection of traffic signs, which is divided into two stages. After a picture has been preprocessed to emphasize relevant information, signs are segmented based on color thresholding, shape-based detection. The second task is the recognition of traffic signs. There are two steps involved in this method. In this study, Histogram of Oriented Gradient is utilized as a feature extractor, and Random Forest Classifier is used in the recognition stage. The findings of the experiment show that utilizing Random Forest Classifier resulted in an accuracy score of 95.59 %, precision of 97.55 %, recall of 95.37% in the recognition process and 90.34 % accuracy in the detection process.

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“…This technique improves the accuracy of the detection. For picture pre-processing, this article primarily employs image enhancement and color space conversion [19].…”

Section: Pre-processing Stagementioning

confidence: 99%