Application of Biomimetic Optimization Algorithm in Computer Vision Object Detection System

Currently, computer vision object detection technology is rapidly developing in multiple fields. However, under limited memory conditions, computer vision object detection technology still faces problems such as difficulty in deploying a large number of models, long training cycles, poor real-time detection, difficulty in accurately locating prominent objects, and blurred boundaries. The biomimetic intelligent optimization algorithm is a goal optimal algorithm that simulates the survival and development patterns of organisms in nature, and can efficiently solve complex optimization problems. Using biomimetic intelligent optimization algorithms to solve complex object detection problems is a promising method. This article focused on the theory and methods of applying ABC optimization algorithms to image processing, and proposed a new approach to object detection in computer vision. The experimental results showed that the ABC optimization algorithm had high convergence accuracy and short calculation time. Moreover, in motion shadow detection, the shadow detection rate and shadow resolution reached 96.9% and 97.3%, respectively. It can be seen that bionic optimization algorithm has fast convergence speed and high search performance in computer vision target detection system, and the algorithm in this paper shows stable and good effect in moving shadow detection. These results and findings provide useful reference and guidance for object detection research in the field of computer vision.