Underwater Target Detection Based on Reinforcement Learning and Ant Colony Optimization

Underwater optical imaging produces images with high resolution and abundant information and hence has outstanding advantages in short-distance underwater target detection. However, low-light and high-noise scenarios pose great challenges in underwater image and video analyses. To improve the accuracy and anti-noise performance of underwater target image edge detection, an underwater target edge detection method based on ant colony optimization and reinforcement learning is proposed in this paper. First, the reinforcement learning concept is integrated into artificial ants' movements, and a variable radius sensing strategy is proposed to calculate the transition probability of each pixel. These methods aim to avoid undetection and misdetection of some pixels in image edges. Second, a double-population ant colony strategy is proposed, where the search process takes into account global search and local search abilities. Experimental results show that the algorithm can effectively extract the contour information of underwater targets and keep the image texture well and also has ideal anti-interference performance.