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Smart city article details

Title	Data-Augmented Deep Learning Models For Abnormal Road Manhole Cover Detection
ID_Doc	17387
Authors	Zhang D.; Yu X.; Yang L.; Quan D.; Mi H.; Yan K.
Year	2023
Published	Sensors, 23, 5
DOI	http://dx.doi.org/10.3390/s23052676
Abstract	Anomalous road manhole covers pose a potential risk to road safety in cities. In the development of smart cities, computer vision techniques use deep learning to automatically detect anomalous manhole covers to avoid these risks. One important problem is that a large amount of data are required to train a road anomaly manhole cover detection model. The number of anomalous manhole covers is usually small, which makes it a challenge to create training datasets quickly. To expand the dataset and improve the generalization of the model, researchers usually copy and paste samples from the original data to other data in order to achieve data augmentation. In this paper, we propose a new data augmentation method, which uses data that do not exist in the original dataset as samples to automatically select the pasting position of manhole cover samples and predict the transformation parameters via visual prior experience and perspective transformations, making it more accurately capture the actual shape of manhole covers on a road. Without using other data enhancement processes, our method raises the mean average precision (mAP) by at least 6.8 compared with the baseline model. © 2023 by the authors.
Author Keywords	convolutional neural network; data augmentation; deep learning; object detection; road manhole cover

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