OUR PROPOSED SYSTEM OPERATES IN REAL TIME , ENHANCING THE OBJECT DETECTION CAPABILITIES OF AUTONOMOUS VEHICLES .
INDUSTRY WATCH
OUR PROPOSED SYSTEM OPERATES IN REAL TIME , ENHANCING THE OBJECT DETECTION CAPABILITIES OF AUTONOMOUS VEHICLES .
Now , a team of researchers have developed a novel IoT-enabled Deep Learning-based end-to-end 3D object detection system with improved detection capabilities even under unfavorable conditions .
This study is said to mark a ‘ significant step ’ in autonomous vehicle object detection technology .
A critical requirement in the operation of autonomous vehicles is their ability to detect and navigate around obstacles , pedestrians and other vehicles across diverse environments .
Current autonomous vehicles employ smart sensors such as LiDARs ( Light Detection and Ranging ) for a 3D view of the surroundings and depth information , RADaR ( Radio Detection and Ranging ) for detecting objects at night and cloudy weather and a set of cameras for providing RGB images and a 360-degree view – collectively forming a comprehensive dataset known as point cloud .
However , these sensors often face challenges like reduced detection capabilities in adverse weather , on unstructured roads , or due to occlusion .
A groundbreaking study could pave the way for a widespread adoption of autonomous vehicles and , in turn , more environmentfriendly and comfortable modes of transport .
Autonomous vehicles require object detection systems to navigate traffic and avoid obstacles on the road .
However , current detection methods often suffer from diminished detection capabilities due to bad weather , unstructured roads or occlusion .
To overcome these shortcomings , an international team of researchers led by Professor Gwanggil Jeon from the Department of Embedded Systems Engineering at Incheon National University ( INU ), Korea , has recently developed a groundbreaking IoTenabled Deep Learning-based end-to-end 3D object detection system .
“ Our proposed system operates in real time , enhancing the object detection capabilities of autonomous vehicles , making navigation through traffic smoother and safer ,” said Prof . Jeon .
The proposed innovative system is built on the YOLOv3 ( You Only Look Once ) Deep Learning object detection
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