DEVELOPMENT OF A DEEP CONVOLUTIONAL NEURAL NETWORK BASED SYSTEM FOR OBJECT RECOGNITION IN VISIBLE LIGHT AND INFRARED IMAGES
DEVELOPMENT OF A DEEP CONVOLUTIONAL NEURAL NETWORK BASED SYSTEM FOR OBJECT RECOGNITION IN VISIBLE LIGHT AND INFRARED IMAGES
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Date
2017-01
Authors
IBRAHIM, YUSUF
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Abstract
This 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.
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A 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, NIGERIA
Keywords
DEVELOPMENT,, DEEP CONVOLUTIONAL NEURAL NETWORK BASED SYSTEM,, OBJECT RECOGNITION,, VISIBLE LIGHT,, INFRARED IMAGES,