DNNs
The dataset consists of around 335K real images equally distributed among 7 classes. The classes represent different levels of rain intensity, namely "Clear", "Slanting Heavy Rain", "Vertical Heavy Rain", "Slanting Medium Rain", "Vertical Medium Rain", "Slanting Low Rain", and "Vertical Low Rain". The dataset has been acquired during laboratory experiments and simulates a low-altitude flight. The system consists of a visual odometry system comprising a processing unit and a depth camera, namely an Intel Real Sense D435i.
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This paper aims to improve the existing techniques on X-ray image inspection of aerial engine by using artificial intelligence (AI) based object detection model. This technique seeks to augment and improve existing automated non-destructive testing (NDT) diagnosis of metal structure of engine parts. Traditional jet-engine maintenance and overhaul processes are resorted to NDT to find defects in internal welds. An application of deep learning for NDT technology can effectively identify presence and location of up to eight types of defects, leading to enhanced work quality and efficiency.
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Research on damage detection of road surfaces has been an active area of research, but most studies have focused so far on the detection of the presence of damages. However, in real-world scenarios, road managers need to clearly understand the type of damage and its extent in order to take effective action in advance or to allocate the necessary resources. Moreover, currently there are few uniform and openly available road damage datasets, leading to a lack of a common benchmark for road damage detection.
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