A non-destructive automatic pavement damage detection scheme based on end-to-end neural networks with multi-level attention mechanism

The accurate classification and statistics of road damage detection technology are crucial for road condition evaluation and maintenance decisions. However, the accuracy of complex road surface damage detection based on deep learning is still insufficient for real engineering, and even one of the damages may be repeatedly counted. This study develops a new non-destructive automatic road damage detection technology that includes detect road damage based on deep learning and redundant damage image de-duplication. This technology based on multi-level attention mechanism is designed from the perspectives of convolutional kernels and loss functions, improves the accuracy of real road surface damage detection. Compared to the original network, [email protected] and F1 score increase by 5.1 % and 4 % for the public dataset RDD-2020, respectively. This technology achieves de-duplicate accuracy of 94.29 % in the duplicate road damage dataset (DRDD) by adding image processing algorithm, which will accelerate the engineering application of non-destructive automatic pavement damage detection.