论文标题
使用机器学习算法和卷积神经网络方法的全球图像分割过程用于自动驾驶车辆
Global Image Segmentation Process using Machine Learning algorithm & Convolution Neural Network method for Self- Driving Vehicles
论文作者
论文摘要
在自动驾驶中，技术图像分割是视觉感知的主要问题。此图像分割过程主要用于医疗应用。在这里，我们采用了一个图像分割过程来视觉感知任务，以预测周围环境上的代理，识别道路边界并跟踪线条标记。本文的主要目的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配，以有效地视觉感知结果。为了采样，假设使用Python语言在Jupyter笔记本中完成的当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计划，以标准并进一步开发最先进的方法，以进行视觉检查系统的理解。实验结果达到73％的含义。我们的方法还达到了90 FPS推理速度，并使用NVDIA GEFORCE GTX 1050 GPU。
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.
