IoT-Embedded Traffic Cones with CNN-based Object Detection to Roadwork Safety

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Abstract

Motorists and construction workers both need to use extreme caution while passing through roadwork zones. This research provides a novel strategy for increasing the security of roadwork by combining Internet of Things (IoT) devices with object recognition through Convolutional Neural Networks (CNNs). To improve communication and data sharing with a centralized control center, this technology equips conventional traffic cones with integrated IoT devices. Traffic cones with IoT sensors track things like their location, the brightness of the surrounding area, and the volume of vehicles on the road. Useful information about the roadwork environment is supplied wirelessly from the cones to the control center. Live video feeds from the IoT-enabled traffic cones are analyzed using a CNN-based object identification algorithm. The model can view and follow vehicles, pedestrians, and other possible threats in a road construction zone. Construction workers and traffic management officials are immediately notified of potential safety hazards through real-time object detection warnings. In terms of roadwork safety, this integrated system has several advantages, such as enhanced knowledge of the current situation, faster identification of potential dangers, and more effective traffic management. Roadwork zones may be made safer for employees and the public using IoT technology and cutting-edge computer vision methods.

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