A REVIEW ON VEHICLE SPEED CONTROLLING AND POTHOLE DETECTION SYSTEM

Computer Science

Road transport is considered as one of the major aspects of promoting Nigeria’s economy. It is also considered as a basis of social and political interaction among the citizenry. There are many causes of road traffic accidents but the major ones are over speeding and potholes, bumps. In effort to reduce over speeding while driving globally, a Driver Support System (DSS) was proposed recently. To improve DSS performance in aiding the drivers to see and recognize the traffic signs, a Computer Image Vision Feature extraction was developed. Popular techniques used in these methods include Histogram Oriented Gradients (HOG) which was good for detection of pedestrians and Scale Invariant Feature Transform (SIFT) that was good in classification. However, this method has the limitation of being based on hand-crafted features extraction which makes it a semi-manual method. Accuracy is another issue associated with the mentioned method. A more accurate and faster approach is needed to quickly and accurately detects and classifies the traffic signs. With the emergence of machine learning algorithms, researchers make improvements in the detection and classification of road traffic signs. Hence, this paper conducts a survey on machine learning approach used for vehicle speed controlling and pothole detection. Hence, this review can aid researchers identified future opportunities that requires more attention

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.