Electronics, Volume 10, Issue 22
2021 November-2 - 158 articles
Cover Story: This research describes an autonomous road inspection system that uses developments in convolutional neural networks to detect road damage. The improved convolutional neural network is implemented in a UAV that runs on a robot operating system and is programmed to fly autonomously by detecting and tracking the yellow lane on the road using Python code. The UAV's job is to fly autonomously on the yellowlane and detect potholes and cracks so that road damage can be reported to the server through 5G or Wi-Fi. The detection model is enhanced in terms of accuracy and compared to the default model in the paper. The updated model may be used in any vehicle, to report road damage in real time and reduce road inspection time by employing autonomous navigation. View this paper - Issues are regarded as officially published after their release is announced to the table of contents alert mailing list .
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