Deep Learning–Based Fully Automated Pavement Crack Detection on 3D Asphalt Surfaces with an Improved CrackNet

Abstract

CrackNet is the result of an 18-month collaboration within a 1-person team to develop a deep learning–based pavement crack detection software that demonstrated successes in terms of consistency for both precision and bias. This paper proposes an improved architecture of CrackNet called CrackNet II for enhanced learning capability and faster performance. The proposed CrackNet II represents two major modifications on the original CrackNet. First, the feature generator, which provides handcrafted features through fixed and nonlearnable procedures, is no longer used in CrackNet II. Consequently, all layers in CrackNet II have learnable parameters that are tuned during the learning process. Second, CrackNet II has a deeper architecture with more hidden layers but fewer parameters. Such an architecture yields five times faster performance compared with the original CrackNet. Similar to the original CrackNet, CrackNet II still uses invariant image width and height through all layers to place explicit requirements on pixel-perfect accuracy. In addition, the combination of a convolution layer and a 1×1 convolution layer was repeated in CrackNet II to learn local motifs with different sizes of local receptive fields. CrackNet II was trained with 2,5 diverse example images and then demonstrated to outperform the original CrackNet. The experiment using 2 testing images showed that CrackNet II performs generally better than the original CrackNet in terms of both precision and recall. The overall precision, recall, and F-measure achieved by CrackNet II for the 2 testing images were 9.2, 89.6, and 89.62%, respectively. Compared with the original CrackNet, CrackNet II is capable of detecting more fine or hairline cracks, while eliminating more local noises and maintaining much faster processing speed.