The function of pheromone in image edge detection algorithm based on ant colony optimization is not obvious and it is difficult to obtain the global optimal solution, thus reducing the accuracy and efficiency of the target edge detection. An Ant Colony Optimization(ACO) based on Bacterial Chemotaxis(BC) is proposed to improve the performance of edge detection. Firstly, the best solution is found through bacterial chemotaxis to produce the initial value of pheromone. Then, the initial value of pheromone obtained from BC is used as the initial pheromone of ACO, to calculate the walking probability of each ant and choose the walking path. When ants experience a pixel, local pheromones are updated. After all the ants complete the iteration, they update the global pheromone and search for the global optimal solution. Finally, according to the relationship between the optimal solution of pheromone and the threshold, the edge and non-edge are obtained. The results show that the proposed method has a great improvement in search accuracy, optimization speed and stability. Compared with other edge detection algorithms, it has better edge continuity, clarity and detection accuracy for small edges with perfect convergence speed.