In autonomous Vehicles technology Image segmentation was a major problem in visual perception. This image segmentation process is mainly used in medical applications. Here we adopted an image segmentation process to visual perception tasks for predicting the agents on the surrounding environment, identifying the road boundaries and tracking the line markings. Main objective of the paper is to divide the input images using the image segmentation process and Convolution Neural Network method for efficient results of visual perception. For Sampling assume a local city data-set samples and validation process done in Jupyter Notebook using Python language. We proposed this image segmentation method planning to standard and further the development of state-of-the art methods for visual inspection system understanding. The experimental results achieves 73% mean IOU. Our method also achieves 90 FPS inference speed and using a NVDIA GeForce GTX 1050 GPU.

中文翻译：

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使用机器学习算法和卷积神经网络方法的自动驾驶车辆全局图像分割过程

在自动驾驶汽车技术中，图像分割是视觉感知中的一个主要问题。这种图像分割过程主要用于医疗应用。在这里，我们对视觉感知任务采用了图像分割过程，以预测周围环境中的代理、识别道路边界和跟踪线标记。本文的主要目标是使用图像分割过程和卷积神经网络方法对输入图像进行分割，以获得有效的视觉感知结果。对于抽样，假设使用 Python 语言在 Jupyter Notebook 中完成本地城市数据集样本和验证过程。我们提出了这种图像分割方法，旨在标准化和进一步开发用于视觉检测系统理解的最先进方法。实验结果达到了 73% 的平均 IOU。我们的方法还使用 NVDIA GeForce GTX 1050 GPU 实现了 90 FPS 的推理速度。