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

📄 Project 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 co...