2 research outputs found
RoadScan: A Novel and Robust Transfer Learning Framework for Autonomous Pothole Detection in Roads
This research paper presents a novel approach to pothole detection using Deep
Learning and Image Processing techniques. The proposed system leverages the
VGG16 model for feature extraction and utilizes a custom Siamese network with
triplet loss, referred to as RoadScan. The system aims to address the critical
issue of potholes on roads, which pose significant risks to road users.
Accidents due to potholes on the roads have led to numerous accidents. Although
it is necessary to completely remove potholes, it is a time-consuming process.
Hence, a general road user should be able to detect potholes from a safe
distance in order to avoid damage. Existing methods for pothole detection
heavily rely on object detection algorithms which tend to have a high chance of
failure owing to the similarity in structures and textures of a road and a
pothole. Additionally, these systems utilize millions of parameters thereby
making the model difficult to use in small-scale applications for the general
citizen. By analyzing diverse image processing methods and various
high-performing networks, the proposed model achieves remarkable performance in
accurately detecting potholes. Evaluation metrics such as accuracy, EER,
precision, recall, and AUROC validate the effectiveness of the system.
Additionally, the proposed model demonstrates computational efficiency and
cost-effectiveness by utilizing fewer parameters and data for training. The
research highlights the importance of technology in the transportation sector
and its potential to enhance road safety and convenience. The network proposed
in this model performs with a 96.12 % accuracy, 3.89 % EER, and a 0.988 AUROC
value, which is highly competitive with other state-of-the-art works.Comment: 6 pages, 5 figures, Accepted at the IEEE 7th Conference on
Communication and Information Technology 202
Drone-Enabled Load Management for Solar Small Cell Networks in Next-Gen Communications Optimization for Solar Small Cells
In recent years, the cellular industry has witnessed a major evolution in
communication technologies. It is evident that the Next Generation of cellular
networks(NGN) will play a pivotal role in the acceptance of emerging IoT
applications supporting high data rates, better Quality of Service(QoS), and
reduced latency. However, the deployment of NGN will introduce a power overhead
on the communication infrastructure. Addressing the critical energy constraints
in 5G and beyond, this study introduces an innovative load transfer method
using drone-carried airborne base stations (BSs) for stable and secure power
reallocation within a green micro-grid network. This method effectively manages
energy deficit by transferring aerial BSs from high to low-energy cells,
depending on user density and the availability of aerial BSs, optimizing power
distribution in advanced cellular networks. The complexity of the proposed
system is significantly lower as compared to existing power cable transmission
systems currently employed in powering the BSs. Furthermore, our proposed
algorithm has been shown to reduce BS power outages while requiring a minimum
number of drone exchanges. We have conducted a thorough review on real-world
dataset to prove the efficacy of our proposed approach to support BS during
high load demand timesComment: 5 pages, 3 figures, 1 table, 1 algorith