Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review.

Concrete crack detection using context‐aware deep semantic segmentation network

A cost effective solution for pavement crack inspection using cameras and deep neural networks

Densely connected deep neural network considering connectivity of pixels for automatic crack detection

Automated crack detection based on image processing is widely used when inspecting concrete structures. The existing methods for crack detection are not yet accurate enough due to the diff...

https://onlinelibrary.wiley.com/doi/pdf/10.1111/mice.12564

Automatic detection method of cracks from concrete surface imagery using two‐step light gradient boosting machine

Attention augmentation with multi-residual in bidirectional LSTM

Computer-vision methods have been extensively used in intelligent transportation systems for vehicle detection. However, the detection of severely occluded or partially observed vehicles due to the limited camera fields of view remains a challenge. This paper presents a multi-camera vehicle detection system that significantly improves the detection performance under occlusion conditions. The key elements of the proposed method include a novel multi-view region proposal network that localizes the candidate vehicles on the ground plane. We also infer the vehicle position on the ground plane by leveraging multi-view cross-camera context. Experiments are conducted on dataset captured from a roadway in Richardson, TX, USA, and the system attains 0.7849 Average Precision and 0.7089 Multi Object Detection Precision. The proposed system results in an approximately 31.2% increase in AP and 8.6% in MODP than the single-camera methods.

/pdf/accurate-vehicle-detection-using-multi-camera-data-fusion-530t4sfpas.pdf

Accurate Vehicle Detection Using Multi-camera Data Fusion and Machine Learning

our task of video copy detection system aims to locate vicdeo segments that are partially copied or near-duplicated versions from an archive of reference videos. In 2010, video copy detection problem was sometimes considered as a solved problem, since previous research within this area used either small-scale or large-scale datasets (e.g. TRECVID 2009, Muscle-VCD) with pre-defined simulated videos. Therefore, the near-perfect results obtained on these datasets were somehow not convincing. As a result, in this paper, we introduce an effective real-scenario video copy detection system which aims to effectively and efficiently detect complex real video copies. Our system obtains decent results on a real-scenario large-scale video copy database (VCDB) generated in 2014, and measures the trade-off between effectiveness and efficiency. We believe our work can be regarded as the beginning for this challenging problem.

Effective real-scenario Video Copy Detection

Long Short-Term Memory (LSTM) has been proven an efficient way to model sequential data, because of its ability to overcome the gradient diminishing problem during training. However, due to the limited memory capacity in LSTM cells, LSTM is weak in capturing long-time dependency in sequential data. To address this challenge, we propose an Attention-aware Bidirectional Multi-residual Recurrent Neural Network (ABMRNN) to overcome the deficiency. Our model considers both past and future information at every time step with omniscient attention based on LSTM. In addition to that, the multi-residual mechanism has been leveraged in our model which aims to model the relationship between current time step with further distant time steps instead of a just previous time step. The results of experiments show that our model achieves state-of-the-art performance in classification tasks.

Xinxiang Zhang

Papers

Concrete crack detection using context‐aware deep semantic segmentation network

Attention augmentation with multi-residual in bidirectional LSTM

Accurate Vehicle Detection Using Multi-camera Data Fusion and Machine Learning

Effective real-scenario Video Copy Detection

An Attention-aware Bidirectional Multi-residual Recurrent Neural Network (Abmrnn): A Study about Better Short-term Text Classification