An Integrated Approach for Vehicle Semantic Segmentation Based on Reinforcement Learning
Accurate and efficient semantic segmentation is a fundamental task in autonomous driving,human-computer interaction,and robot vision applications.Due to the complexity of the scenes,traditional segmentation methods may en-counter difficulties in handling complex backgrounds and noise,requiring manual parameter tuning to adapt to different im-age scenarios,leading to significant human and time costs.Reinforcement learning can interact with the environment to learn autonomously,discover image features and rules,and alleviate the impact of noise and complex backgrounds on im-age segmentation,reducing dependence on manual feature engineering.This paper adopts a sliding window-based seg-mentation method and introduces reinforcement learning to achieve vehicle segmentation in road scenes.Under this method,the robot improves the accuracy and average intersection-over-union of vehicle segmentation through autonomous learning.
万方数据
维普
