Modified Genetic Algorithm and Blob Analysis for Solving Blank Spot Zone in Aerial Mapping

Identifying blank spots using a blob analysis mechanism and flight route planning using genetic algorithms is a proposed method for solving the blank spot zone. It has a high possibility of leaving a blank spot zone. The blank spot zone, which is larger than the camera coverage area, creates the possibility for some areas of the blank spot zone not to be included in the camera coverage during the blank spot zone capture mission. The uncaptured area causes imperfections to the obtained map images, thus requiring additional flight missions to recapture the areas indicated by the blank spot zone. Then, the computation of the flight route, which runs onboard when the UAV flies, becomes one of the determining factors for flight duration and battery consumption. To increase the convergence speed in getting the total shortest distance and reduce the possibility of a blank spot zone appearing during the flight mission, we proposed modifying the initial population in the genetic algorithm and the blank spot zone handling mechanism. The proposed method includes combining the genetic algorithm with the nearest neighbor heuristic method and the implementation of image area division and image reduction in the blob analysis method. The simulation results show that the proposed genetic algorithm can increase 60% of convergence consistency level, increase the convergence speed and increase two times shorter in reaching the initial shortest distance. Moreover, the final proposed system result can remove the possibility of repeated blank spot zones appearing.