Neural Networks for Computer Vision

Implementing Neural Networks for Computer Vision in Autonomous Vehicles and Robotics, for Object Detection and Object Tracking, Object Classification, Pattern Recognition, Robotics Control. From Very Beginning to Complex and Huge Project.

Reference to:

Valentyn N Sichkar. Neural Networks for computer vision in autonomous vehicles and robotics // GitHub platform. DOI: 10.5281/zenodo.1317904

Structure of repository

Research papers
Related works
Description
Empirical Examples
Content
- Theory and experimental results
- Codes

Research papers

Sichkar V. N. "Reinforcement Learning Algorithms in Global Path Planning for Mobile Robot", 2019 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), Sochi, Russia, 2019, pp. 1-5. doi: 10.1109/ICIEAM.2019.8742915 (Full-text available also here ResearchGate.net/profile/Valentyn_Sichkar)
Sichkar V. N. Effect of various dimension convolutional layer filters on traffic sign classification accuracy. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2019, vol. 19, no. 3, pp. DOI: 10.17586/2226-1494-2019-19-3-546-552 (Full-text available also here https://www.researchgate.net/profile/Valentyn_Sichkar)
Sichkar V.N. Comparison analysis of knowledge based systems for navigation of mobile robot and collision avoidance with obstacles in unknown environment. St. Petersburg State Polytechnical University Journal. Computer Science. Telecommunications and Control Systems, 2018, Vol. 11, No. 2, Pp. 64–73. DOI: 10.18721/JCSTCS.11206 (Full-text available also here https://www.researchgate.net/profile/Valentyn_Sichkar)

Related works

The study on image processing in Python is put in separate repository and is available here: https://github.com/sichkar-valentyn/Image_processing_in_Python
The study of Semantic Web languages OWL and RDF for Knowledge representation of Alarm-Warning System is put in separate repository and is available here: https://github.com/sichkar-valentyn/Knowledge_Base_Represented_by_Semantic_Web_Language
The research results for Neural Network Knowledge Based system for the tasks of collision avoidance is put in separate repository and is available here: https://github.com/sichkar-valentyn/Matlab_implementation_of_Neural_Networks
The research on Machine Learning Algorithms and techniques in Python is put in separate repository and is available here: https://github.com/sichkar-valentyn/Machine_Learning_in_Python

Description

The main aim of the repository is to study and to develope complex project on Computer Vision in autonomous vehicles and robotics through basics in Neural Networks to advanced learning. Here is brief description of repository, its stages of development, animated figures with empirical results. To get full content scroll down or click here to reach the content.

Empirical Examples

Example #1 - simple convolving of input image with three different filters for edge detection.

Example #2 - more complex Convolving of input image with following architecture:
Input ---> Conv --> ReLU --> Pool ---> Conv --> ReLU --> Pool ---> Conv --> ReLU --> Pool

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🚩 Concept Map of the Course

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Example #4 - image Classification with CNN and CIFAR-10 datasets in pure numpy, algorithm and file structure:

Example #5 - training of Model #1 for CIFAR-10 Image Classification:

Example #6 - Initialized Filters and Trained Filters for ConvNet Layer for CIFAR-10 Image Classification task:

Example #7 - training of Model #1 for MNIST Digits Classification:

Example #8 - Initialized Filters and Trained Filters for ConvNet Layer for MNIST Digits Classification:

Example #9 - Histogram of 43 classes for training dataset with their number of examples for Traffic Signs Classification before and after Equalization by adding transformated images from original dataset:

Example #10 - Prepared and preprocessed data for Traffic Sign Classification (RGB, Gray, Local Histogram Equalization):

Example #11 - Implementing Traffic Sign Classification with Convolutional Neural Network.
- Left: Original frame with Detected Sign.
- Upper Right: cut frame with Detected Sign.
- Lower Right: classified frame by ConvNet according to the Detected Sign.