  Pixel-level road crack detection has always been a challenging task in
intelligent transportation systems. Due to the external environments, such as
weather, light, and other factors, pavement cracks often present low contrast,
poor continuity, and different sizes in length and width. However, most of the
existing studies pay less attention to crack data under different situations.
Meanwhile, recent algorithms based on deep convolutional neural networks
(DCNNs) have promoted the development of cutting-edge models for crack
detection. Nevertheless, they usually focus on complex models for good
performance, but ignore detection efficiency in practical applications. In this
article, to address the first issue, we collected two new databases (i.e.
Rain365 and Sun520) captured in rainy and sunny days respectively, which enrich
the data of the open source community. For the second issue, we reconsider how
to improve detection efficiency with excellent performance, and then propose
our lightweight encoder-decoder architecture termed CarNet. Specifically, we
introduce a novel olive-shaped structure for the encoder network, a
light-weight multi-scale block and a new up-sampling method in the decoder
network. Numerous experiments show that our model can better balance detection
performance and efficiency compared with previous models. Especially, on the
Sun520 dataset, our CarNet significantly advances the state-of-the-art
performance with ODS F-score from 0.488 to 0.514. Meanwhile, it does so with an
improved detection speed (104 frame per second) which is orders of magnitude
faster than some recent DCNNs-based algorithms specially designed for crack
detection.
