Research and implementation of path planning based on optimized semantic segmentation technology

Abstract Aiming at the problem of inaccurate path planning in complex disaster environments during urban emergency rescue, an innovative approach is proposed by combining the optimized semantic segmentation technology with the particle swarm optimization algorithm to solve the path planning problem. Firstly, this paper analyzes the issue of insufficient feature expression ability of traditional semantic segmentation technology when recognizing urban disaster images. Then, in response to these deficiencies, the semantic segmentation technology is optimized, and the optimized technology is combined with the improved particle swarm optimization algorithm for path planning. Finally, through a practical case, the optimized semantic segmentation technology is applied to the analysis of remote sensing images in key areas. By combining it with the path planning algorithm, an effective scheduling strategy can be formulated, providing dynamic decision-making support for the emergency command center. This research has important practical value for improving the intelligence level of urban emergency rescue systems.