Encoder-decoder network for pixel-level road crack detection in black-box images

Timely monitoring of pavement cracks is essential for successful maintenance of road infrastructure. Accurate information concerning crack location and severity enables proactive management of the infrastructure. Black-box cameras, which are becoming increasingly widespread at an affordable price, can be used as efficient road-image collectors over a wide area. However, the cracks in these images are difficult to detect, because the images containing them often include objects other than roads. Thus, we propose a pixel-level detection method for identifying road cracks in black-box images using a deep convolutional encoder-decoder network. The encoder consists of convolutional layers of the residual network for extracting crack features, and the decoder consists of deconvolutional layers for localizing the cracks in an input image. The proposed network was trained on 427 out of 527 images extracted from black-box videos and tested on the remaining 100 images. Compared with VGG-16, ResNet-50, ResNet-101, ResNet-200 with transfer learning, and ResNet-152 without transfer learning, ResNet-152 with transfer learning exhibited the best performance, achieving recall, precision, and intersection of union of 71.98%, 77.68%, and 59.65%, respectively. The experimental results prove that the proposed method is optimal for detecting cracks in black-box images at the pixel level.