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.

翻译：像素级道路裂缝检测一直是智能交通系统中的一项挑战性任务。由于天气、光照等外部环境因素，路面裂缝往往呈现低对比度、连续性差以及长短宽度不一的特点。然而，现有研究大多较少关注不同情境下的裂缝数据。同时，基于深度卷积神经网络的最新算法推动了裂缝检测前沿模型的发展，但这些模型通常侧重于追求高性能的复杂架构，却忽略了实际应用中的检测效率。针对第一个问题，本文收集了分别在雨天和晴天拍摄的两个新数据库（即Rain365和Sun520），丰富了开源社区的数据资源。针对第二个问题，我们重新思考如何在保持卓越性能的同时提升检测效率，进而提出了一种轻量级编码器-解码器架构，命名为CarNet。具体而言，我们在编码器网络中引入了一种新颖的橄榄形结构，在解码器网络中设计了轻量级多尺度模块和新的上采样方法。大量实验表明，与现有模型相比，我们的模型能更好地平衡检测性能与效率。特别是在Sun520数据集上，CarNet将ODS F分数从0.488显著提升至0.514，达到了最先进水平。同时，其检测速度提升至每秒104帧，比近期专门为裂缝设计的基于深度卷积神经网络的算法快数个数量级。