Research on the application of multi-objective optimization algorithm for complex scenes in computer vision

In computer vision tasks, such as target detection and segmentation, it is often necessary to balance the conflicting performance indexes such as accuracy and speed. However, the existing multi-objective optimization algorithm (MOO) has some problems in dealing with complex scenes, such as the lack of dynamic objective function, insufficient constraint processing mechanism and limited generalization ability. In order to solve these problems, this paper proposes a Dynamic Multi-Objective Optimization for Complex Scenarios (DMOO-CS) algorithm, which mainly includes the following innovations: First, it constructs a Scene Complexity Prediction Network (SCP-NET), evaluates the scene complexity factors in real time through CNN-LSTM model, and dynamically adjusts the weight of optimization objectives, Secondly, the adaptive constraint processing module is designed, and the Context-Aware Constraint Relaxation mechanism (CACR) and constraint priority ranking algorithm are introduced to dynamically adjust the constraint threshold and optimization order. In addition, this paper also puts forward a cross-scene transfer learning strategy, and pre-trains SCP-Net through a meta-learning framework to improve the generalization ability of the model in different scenarios. The experimental results show that DMOO-CS shows better dynamic weight adjustment ability and constraint processing performance in complex scenes, and has stronger adaptability and robustness in cross-scene migration, which significantly improves the performance and stability of visual tasks in complex environments.