Applying Nature-Inspired Optimization: Ant and Bee Colony Approaches for Enhanced Edge Detection in Image Processing

Finding boundaries within images is the aim of edge detection, a basic job in computer vision, image processing, and pattern recognition. It can be difficult to detect edges in noisy images or under different situations using traditional edge detection techniques like the Sobel and Canny edge detectors. Motivated by the collective behavior of ants and bees in nature, this work investigates the use of Ant Colony Optimization (ACO) and Bee Colony Optimization (BCO) algorithms for edge identification. For difficult tasks like edge detection, these swarm intelligence techniques can efficiently search through vast solution spaces and offer the best answers. The ACO algorithm investigates potential edge sites and optimizes them using pheromone updates by simulating the foraging behavior of ants. However, the BCO algorithm uses the exploration and exploitation behavior of the bees to identify specific boundaries in the image. We compare how well the two systems detect edges in pictures with different noise levels and image quality. The findings demonstrate that edge detection performance is much enhanced by both ACO and BCO, with BCO offering quicker convergence and more precise edge localization. The study shows that ACO and BCO have the potential to be useful tools for detecting edges in noisy and complex images.