| dc.description.abstract | Most of the construction sites in Sri
Lanka work under unsafe conditions due to
limited resources. Due to these unsafe conditions,
human lives are in danger at times. The
construction industry holds a major position in
the development process of Sri Lanka, as it
significantly contributes, not only for Gross
Domestic Product but also for Gross National
Product. Unfortunately, the Health and Safety
factors have become a secondary concern though
the construction industry holds a major portion
in the economy of the country. The traditional
inspection methods currently practised in the
industry seem to be outdated, time-consuming,
less efficient, less effective, and increase the
workload of safety officers. It is impossible to
perform observations in multiple locations at the
same time by a single safety officer because some
locations in the sites are hard to reach, and there
may be blind spots too. This study proposes an
automated safety inspection method to increase
the safety levels of construction sites. For this, the
study reveals a comprehensive experimental
discussion on how to blend image processing
techniques with unmanned aerial vehicles. Image
processing is the technical analysis of images by
using complex algorithms, and in this scenario,
unmanned aerial vehicles (drones/quadcopters)
act as a flexible image providing source that can
fly over the construction sites by providing realtime
videos for the algorithm to analyse for safety
hazards. The study was concluded by achieving
two objectives, developing an algorithm with
YOLO v3 architecture to detect safety hazards
through drones, and measuring the accuracy and
reliability of the automated detections. | en_US |
