International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Analysis and Implementation of Computer Vision Techniques for Autonomous Driving

Author : Riya Chougule ¹ Amruta Bhalerao ² Sanika Abhyankar ³ Kajal Pompatwar ⁴ Jitendra Chavan ⁵

Date of Publication :12th August 2021

Abstract: Autonomous driving technology is one of the fastest-growing technologies in these years. Self-driving vehicles help to reduce the number of road accidents happening every year. Various organizations, startups, and researchers are working on this technology. Due to the advances in the field of machine learning in past decades, a major push is received in the field of computer vision and various techniques like object detection, semantic segmentation, etc. are developed. A Large number of open-source datasets are available for training autonomous driving systems. The review intends to provide a deep survey about different computer vision techniques, architecture, and various datasets used for Autonomous driving technology.

Reference :

1. “Semantic Object Segmentation in Tagged Videos via Detection” by Yu Zhang, Xiaowu Chen, Jia Li, Chen Wang, Changqun Xia, Jun Li, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 40, Issue:7, 20 July 2017.
2. “Small Object Sensitive Segmentation of Urban Street Scene with Spatial Adjacency Between Object Classes” by Dazhou Guo, Liang Zhu, Yuhang Lu, Hongkai Yu, Song Wang, IEEE Transactions on Image Processing, Vol. 28, No. 6, June 2019
3. “Vehicle Instance Segmentation From Aerial Image And Video Using a Multitask Learning Residual Fully Convolutional Network” by Lichao Mou, Xiao Xiang Zhu, IEEE Transactions on Geoscience and Remote Sensing, Vol. 56, Issue: 11, Nov. 2018.
4. Di Feng, Christian Haase-Schutz, Lars Rosenbaum, Heinz Hertlein, Claudius Glaser, Fabian Timm, Werner Wiesbeck, Klaus Dietmayer, “Deep multimodel object detection & semantic segmentation for autonomous driving: datasets, methods & challenges”, IEEE Transaction on Intelligent Transportation Systems, Vol. 22, Issue: 3, March 2021
5. Wei Zhou, Julie Stephany Berrio, Stewart Worrall, Eduardo Nebot, “Automated Evaluation of Semantic Segmentation Robustness for Autonomous Driving”, Vol. 21, Issue: 5, May 2020.
6. “Semantic Instance Segmentation for Autonomous Driving” by Bert De Brabandere Davy Neven Luc Van Gool ESAT-PSI, KU Leuven, In 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops.
7. Saurabh Chaudhury, Indrani Pal, “A New Approach for Image Segmentation with MATLAB” Int’l Conf. on Computer & Communication Technology IEEE 2010
8. Debalina Barik, Manik Mondal “Object Identification For Computer Vision using Image Segmentation” IEEE 2010 2nd International Conference on Education Technology and Computer
9. Niall O’Mahony, Sean Campbell, Anderson Carvalho, Suman Harapanahalli, Gustavo Velasco Hernandez, Lenka Krpalkova, Daniel Riordan, and Joseph Walsh (2019). “Deep Learning vs. Traditional Computer Vision” Vol. 943.
10. https://www.analytixlabs.co.in/blog/what-is-imagesegmentation/
11. https://analyticsindiamag.com/my-experiment-withunet-building-an-image-segmentation-model/
12. https://www.allaboutcircuits.com/technicalarticles/two-dimensional-convolution-in-imageprocessing/
13. https://deepomatic.com/en/what-is-image-recognition
14. https://bdd-data.berkeley.edu
15. https://www.anaconda.com
16. https://www.fast.ai
17. https://deeplizard.com/
18. https://towardsdatascience.com/understandingsemantic-segmentation-with-unet-6be4f42d4b47