Otsu's Thresholding and Genetic Algorithm for Road Segmentation in Intelligent Transportation Systems

By integrating the traditional Otsu's thresholding method with modern genetic algorithm optimization, we significantly enhance image segmentation performance. This hybrid approach can be effectively applied to road segmentation in intelligent transportation systems, where it not only improves segmentation accuracy but also dramatically increases computational speed to satisfy real-time processing demands. From an implementation perspective, the genetic algorithm optimizes the threshold selection process by evolving candidate solutions through fitness evaluation, selection, crossover, and mutation operations. The fitness function typically measures between-class variance, which Otsu's method maximizes for optimal threshold determination. This combination enables more precise image segmentation and faster processing of large-scale image datasets. By merging classical algorithms with contemporary intelligent optimization techniques, we achieve superior results in image segmentation applications, thereby contributing to the advancement of intelligent transportation systems. The method demonstrates particular effectiveness in handling complex road scenarios with varying lighting conditions and obstructions.