Detection of impaired objects in roadways using metaheuristic algorithms. (14th June 2022)

• Record Type: Journal Article
• Authors: Balaji, Sambandam Ramachandran; Santhanakrishnan, Karthikeyan; Radhakrishnan, Manikandan; John, Albert Mayan
• Is Part Of: International journal of engineering systems modelling and simulation. Volume 13:Number 3(2022)
• Page Start: 209
• Page End: 217
• Publication Date: 2022-06-14
• Subjects: crack detection -- pothole detection -- metaheuristic algorithm -- particle swarm optimisation -- PSO -- whale optimisation -- grasshopper optimisation; Engineering systems -- Computer simulation -- Periodicals; Engineering systems -- Mathematical models -- Periodicals; 620.0042
• Languages: English
• ISSNs: 1755-9758

Abstract

Roads have become the most fundamental element in land transportation system. In the long run, some malformations will appear on the road, such as potholes and cracks. Since manual inspection is unpredictable, subjective and prolonged, we go for computer vision-based methods. Thus our work focused on the automatic detection of the cracks and potholes. For the detection process, we acquire the video, convert them into frames and use metaheuristic algorithms to implement detection of the roadway damages (i.e., cracks and potholes). The novelty of this approach lies in using texture-based features to differentiate between crack surfaces and intact roads. Three different metaheuristic algorithms are used to detect the crack and potholes. The performance of the algorithms is evaluated using the different parameters. Based on the performance, it is observed that grasshopper optimisation algorithm outperforms well for this application.