一种基于强化学习的车辆语义分割方法

An Integrated Approach for Vehicle Semantic Segmentation Based on Reinforcement Learning

准确高效的语义分割是自动驾驶、人机交互和机器人视觉应用的基本任务。由于在复杂的场景下,传统分割方法可能会出现难以处理的情况,例如无法很好地处理复杂的背景和噪声,需要手动调节参数来适应不同的图像场景导致大量的人力和时间成本。强化学习方法可以通过与环境进行交互学习,自主发现图像特征和规律,减轻噪声和复杂背景等因素对图像分割的影响,减少对人工特征工程的依赖。因此,采用基于滑动窗口的分割方法并引入强化学习,实现对道路场景下车辆的分割。在该方法下,机器人通过自主学习,提高了车辆分割的精度与平均交并比。

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 encounter difficulties in handling complex backgrounds and noise,requiring manual parameter tuning to adapt to different image 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 image segmentation,reducing dependence on manual feature engineering.This paper adopts a sliding window-based segmentation 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.