Global Image Segmentation Process using Machine Learning algorithm & Convolution Neural Network method for Self- Driving Vehicles

TL;DR

This paper presents a convolutional neural network-based image segmentation method for autonomous vehicles, improving visual perception by accurately identifying environment features with high speed and reasonable accuracy.

Contribution

The paper introduces a novel CNN-based image segmentation approach tailored for self-driving cars, achieving real-time performance and significant accuracy improvements.

Findings

Achieved 73% mean IOU in segmentation accuracy.

Attained 90 FPS inference speed on NVIDIA GTX 1050 GPU.

Demonstrated effectiveness in urban driving scenarios.

Abstract

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.