论文标题
深度学习方法的进步，用于路面表面裂纹检测和可见光视觉图像的识别
Advances in deep learning methods for pavement surface crack detection and identification with visible light visual images
论文作者
论文摘要
与NDT和健康监测方法相比，工程结构中的裂纹方法，基于可见光图像的表面裂纹检测或识别是无接触式的，具有快速，低成本和高精度的优势。首先，收集了典型的路面（也是混凝土）裂纹公共数据集，并总结了样本图像的特征以及包括环境，噪声和干扰等的随机可变因素。随后，比较了三种主要裂纹识别方法（即手工制作的功能工程，机器学习，深度学习）的优点和缺点。最后，审查了从模型体系结构的各个方面，测试性能和预测有效性，包括自我建造的CNN，转移学习（TL）和Encoder-Decoder（ED）在内的典型深度学习模型的发展和进步，可以轻松地在嵌入式平台上部署。基准测试表明：1）它已经能够在嵌入式平台上实现实时像素级裂纹识别：图像样本的整个裂纹检测平均时间成本小于100ms，使用ED方法（即FPCNET）或基于InceptionV3的TL方法。基于Mobilenet（轻质骨干基网络），使用TL方法将其减少到小于10ms。 2）就精度而言，CCIC可以达到99.8％以上，这很容易被人眼确定。在SDNET2018上，很难识别出的一些样本，FPCNET可以达到97.5％，而TL方法接近96.1％。 据我们所知，本文首次全面总结了路面裂缝公共数据集，并评估了和评估嵌入式平台的表面裂纹检测和识别深度学习方法的性能和有效性。
Compared to NDT and health monitoring method for cracks in engineering structures, surface crack detection or identification based on visible light images is non-contact, with the advantages of fast speed, low cost and high precision. Firstly, typical pavement (concrete also) crack public data sets were collected, and the characteristics of sample images as well as the random variable factors, including environmental, noise and interference etc., were summarized. Subsequently, the advantages and disadvantages of three main crack identification methods (i.e., hand-crafted feature engineering, machine learning, deep learning) were compared. Finally, from the aspects of model architecture, testing performance and predicting effectiveness, the development and progress of typical deep learning models, including self-built CNN, transfer learning(TL) and encoder-decoder(ED), which can be easily deployed on embedded platform, were reviewed. The benchmark test shows that: 1) It has been able to realize real-time pixel-level crack identification on embedded platform: the entire crack detection average time cost of an image sample is less than 100ms, either using the ED method (i.e., FPCNet) or the TL method based on InceptionV3. It can be reduced to less than 10ms with TL method based on MobileNet (a lightweight backbone base network). 2) In terms of accuracy, it can reach over 99.8% on CCIC which is easily identified by human eyes. On SDNET2018, some samples of which are difficult to be identified, FPCNet can reach 97.5%, while TL method is close to 96.1%. To the best of our knowledge, this paper for the first time comprehensively summarizes the pavement crack public data sets, and the performance and effectiveness of surface crack detection and identification deep learning methods for embedded platform, are reviewed and evaluated.
