Vision-Based Dirt Detection and Adaptive Tiling Scheme for Selective Area Coverage. (30th December 2018)

• Record Type: Journal Article
• Authors: Ramalingam, Balakrishnan; Veerajagadheswar, Prabakaran; Ilyas, Muhammad; Elara, Mohan Rajesh; Manimuthu, Arunmozhi
• Other Names: Lazaro Antonio Academic Editor.
• Is Part Of: Journal of sensors. Volume 2018(2018)
• Publication Date: 2018-12-30
• Subjects: Detectors -- Periodicals; 681.205
• DOI: 10.1155/2018/3035128
• Languages: English
• ISSNs: 1687-725X

Abstract

Abstract : This paper proposes a visual dirt detection algorithm and a novel adaptive tiling-based selective dirt area coverage scheme for reconfigurable morphology robot. The visual dirt detection technique utilizes a three-layer filtering framework which includes a periodic pattern detection filter, edge detection, and noise filtering to effectively detect and segment out the dirt area from the complex floor backgrounds. Then adaptive tiling-based area coverage scheme has been employed to generate the tetromino morphology to cover the segmented dirt area. The proposed algorithms have been validated in Matlab environment with real captured dirt images and simulated tetrominoes tile set. Experimental results show that the proposed three-stage filtering significantly enhances the dirt detection ratio in the incoming images with complex floors with different backgrounds. Further, the selective dirt area coverage is performed by excluding the already cleaned area from the unclean area on the floor map by incorporating the tiling pattern generated by adaptive tetromino tiling algorithm.