DEVELOPMENT OF A DEEP CONVOLUTIONAL NEURAL NETWORK BASED SYSTEM FOR OBJECT RECOGNITION IN VISIBLE LIGHT AND INFRARED IMAGES

Simple item page
dc.contributor.authorIBRAHIM, YUSUF
dc.date.accessioned2017-07-24T08:05:29Z
dc.date.available2017-07-24T08:05:29Z
dc.date.issued2017-01
dc.descriptionA DISSERTATION SUBMITTED TO THE SCHOOL OF POSTGRADUATE STUDIES, AHMADU BELLO UNIVERSITY, ZARIA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF A MASTER OF SCIENCE (MSc) DEGREE IN COMPUTER ENGINEERING DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING FACULTY OF ENGINEERING AHMADU BELLO UNIVERSITY, ZARIA, NIGERIAen_US
dc.description.abstractThis research investigated image recognition frameworks on datasets of visible-light and infrared (IR) imagery using deep convolutional neural networks (CNN). This is due to their recent success on a variety of problems including computer vision which often surpassed the state of the art methods. Three deep learning based object recognition approaches were investigated on a fused version of the images in order to exploit the synergistic integration of the information obtained from varying spectra of same data with a view to improving the overall classification accuracy. Firstly, a simple 3-layer experimental deep network was designed and used to train the datasets for performing recognition. A second experiment was conducted where a pre-trained 16-layer convolutional neural network (Imagenet-vgg-verydeep-16) was used to extract features from the datasets. These features are then used to train a logistic regression classifier for performing the recognition. Finally, an experiment was conducted where another pre-trained model (Imagenet-vgg-f) was fine-tuned to suit the dataset‘s classes and which is then retrained accordingly on the datasets using back propagation. This research adopted a simple and novel fusion strategy where the IR and visible images were fused by concatenating the IR image as an additional fourth layer to the visible image with a view to enhancing the performance of object recognition systems. Despite its simplicity and the limited size of the training data, the 3-layer network achieved a classification accuracies of 86.8590% on the fused multimodal image, 85.0610% on the visible images and 67.9878% on the Infrared images. These results represent a respective improvement by 4.76%, 3.16% and 13.99% when compared withthose of the CNN architectureofZhang. Also, an improvement by 4.06% on visible images was obtained over that of Zhang‘sGnostic Field + CNN model. It is also observed that while the pre-trained model performed well, fine-tuning improved the performance to 100% classification accuracy. Results obtained were compared with those ofZhangas a means of validation. This work was implemented using MATLAB programming language, MatConvnet library for CNN and Liblinear library for large linear classification.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/9094
dc.language.isoenen_US
dc.subjectDEVELOPMENT,en_US
dc.subjectDEEP CONVOLUTIONAL NEURAL NETWORK BASED SYSTEM,en_US
dc.subjectOBJECT RECOGNITION,en_US
dc.subjectVISIBLE LIGHT,en_US
dc.subjectINFRARED IMAGES,en_US
dc.titleDEVELOPMENT OF A DEEP CONVOLUTIONAL NEURAL NETWORK BASED SYSTEM FOR OBJECT RECOGNITION IN VISIBLE LIGHT AND INFRARED IMAGESen_US
dc.typeThesisen_US
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
DEVELOPMENT OF A DEEP CONVOLUTIONAL NEURAL NETWORK BASED SYSTEM FOR OBJECT RECOGNITION IN VISIBLE LIGHT AND INFRARED IMAGES.pdf
Size:
1.64 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.62 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
ENGINEERING