SMART ROAD DAMAGE DETECTION AND WARNING
Author(s):
Sayali bandal, akanksha ekshinge, shrutika gaikwad , prof. gade n. b.
Keywords:
Accident detection, convolutional neural network, computer vision, neural networks.
Abstract
currently, it's difficult to keep up with the framework's development. Viewed by from one end of the world to the other, there are local and public overseers. Consistently monitoring (i.e., measuring the degree of security and unshakable quality) the situation of extremely large designs is a crucial component of effective framework support. Meanwhile, significant progress has recently been made in PC vision, mostly as a result of effective applications of deep learning models. These ingenious developments are making it possible to automate previously difficult-to-mechanize vision tasks, with promising results that could aid administrators in enhancing their framework maintenance efforts. In this exceptional circumstance, the IEEE 2020 global Road Damage Detection (RDD) Challenge is providing an opportunity for researchers in deep learning and computer vision to reach out and assist in accurately tracking asphalt damages on street organizations.To that end, this essay offers the following two commitments: We describe our response to the RDD Challenge in a first section. The efforts we made to communicate our idea to a local street organisation are shown in the next section, along with an explanation of the suggested approach and any challenges we encountered.
Article Details
Unique Paper ID: 158831

Publication Volume & Issue: Volume 9, Issue 10

Page(s): 902 - 904
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