Faculty Publications
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Publications by NITK Faculty
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Item Performance evaluation of deep learning frameworks on computer vision problems(Institute of Electrical and Electronics Engineers Inc., 2019) Nara, M.; Mukesh, B.R.; Padala, P.; Kinnal, B.Deep Learning (DL) applications have skyrocketed in recent years and are being applied in various domains. There has been a tremendous surge in the development of DL frameworks to make implementation easier. In this paper, we aim to make a comparative study of GPU-accelerated deep learning software frameworks such as Torch and TenserFlow (with Keras API). We attempt to benchmark the performance of these frameworks by implementing three different neural networks, each designed for a popular Computer Vision problem (MNIST, CIFAR10, Fashion MNIST). We performed this experiment on both CPU and GPU(Nvidia GeForce GTX 960M) settings. The performance metrics used here include evaluation time, training time, and accuracy. This paper aims to act as a guide to selecting the most suitable framework for a particular problem. The special interest of the paper is to evaluate the performance lost due to the utility of an API like Keras and a comparative study of the performance over a user-defined neural network and a standard network. Our interest also lies in their performance when subjected to networks of different sizes. ©2019 IEEE.Item AMMDAS: Multi-modular generative masks processing architecture with adaptive wide field-of-view modeling strategy(Institute of Electrical and Electronics Engineers Inc., 2020) Desanamukula, V.S.; Chilukuri, P.K.; Padala, P.; Padala, P.; Pvgd, P.R.The usage of transportation systems is inevitable; any assistance module which can catalyze the flow involved in transportation systems, parallelly improving the reliability of processes involved is a boon for day-to-day human lives. This paper introduces a novel, cost-effective, and highly responsive Post-active Driving Assistance System, which is "Adaptive-Mask-Modelling Driving Assistance System" with intuitive wide field-of-view modeling architecture. The proposed system is a vision-based approach, which processes a panoramic-front view (stitched from temporal synchronous left, right stereo camera feed) & simple monocular-rear view to generate robust & reliable proximity triggers along with co-relative navigation suggestions. The proposed system generates robust objects, adaptive field-of-view masks using FRCNN+Resnet-101_FPN, DSED neural-networks, and are later processed and mutually analyzed at respective stages to trigger proximity alerts and frame reliable navigation suggestions. The proposed DSED network is an Encoder-Decoder-Convolutional-Neural-Network to estimate lane-offset parameters which are responsible for adaptive modeling of field-of-view range (1570-2100) during live inference. Proposed stages, deep-neural-networks, and implemented algorithms, modules are state-of-the-art and achieved outstanding performance with minimal loss(L{p, t}, L?, LTotal) values during benchmarking analysis on our custom-built, KITTI, MS-COCO, Pascal-VOC, Make-3D datasets. The proposed assistance-system is tested on our custom-built, multiple public datasets to generalize its reliability and robustness under multiple wild conditions, input traffic scenarios & locations. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.