Pothole Detection by Soft Computing

Subject- Potholes on roads are regarded as serious problems in the transportation domain and ignoring them leads to the increase of accidents, traffic, vehicle fuel consumption and waste of time and energy. As a result, pothole detection has attracted researchers’ attention and different methods have been presented for it up to now. Background- The major part of previous research is based on image processing. They utilize dedicated cameras that are embedded on vehicles to take images and analyze them by massive image processing programs. This scheme requires dedicated hardware that is not typically available on vehicles. Methodology- In this paper, a new scheme is proposed, which uses accelerometer and GPS sensors. These type of sensors are available in today’s smartphones as well as modern vehicles. The data generated by these sensors is processed via soft computing to increase accuracy of pothole detection. The proposed algorithm uses combination of fuzzy system and evolutionary algorithms. Genetic algorithm and harmony search are used for adjusting membership functions of the proposed fuzzy system. Result- For evaluation, a case study has been conducted regarding detecting potholes on Ghaffari Street in Birjand city. Experimental results show the high accuracy of the proposed algorithm in comparison to other solutions. They reveal that the accuracy of the proposed genetic fuzzy algorithm is 98 percent and for the proposed harmony fuzzy algorithm is 99 percent.