  Advancements in artificial intelligence (AI) gives a great opportunity to
develop an autonomous devices. The contribution of this work is an improved
convolutional neural network (CNN) model and its implementation for the
detection of road cracks, potholes, and yellow lane in the road. The purpose of
yellow lane detection and tracking is to realize autonomous navigation of
unmanned aerial vehicle (UAV) by following yellow lane while detecting and
reporting the road cracks and potholes to the server through WIFI or 5G medium.
The fabrication of own data set is a hectic and time-consuming task. The data
set is created, labeled and trained using default and an improved model. The
performance of both these models is benchmarked with respect to accuracy, mean
average precision (mAP) and detection time. In the testing phase, it was
observed that the performance of the improved model is better in respect of
accuracy and mAP. The improved model is implemented in UAV using the robot
operating system for the autonomous detection of potholes and cracks in roads
via UAV front camera vision in real-time.
