Mr. Jianbo Li Profile

Jianbo Li

at Qingdao Univ

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Publications (2)

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SPIE Journal Paper | 24 January 2024

FSDGNet: frequency and spatial dual guide network for crack detection

Yang Liu, Genji Yuan, Jianbo Li, Shuteng Hu

JEI, Vol. 33, Issue 01, 013027, (January 2024) https://doi.org/10.1117/12.10.1117/1.JEI.33.1.013027

KEYWORDS: Feature extraction, Education and training, Fourier transforms, Convolution, Feature fusion, Network architectures, Image segmentation, Performance modeling, Image enhancement, Object detection

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Cracks are one of the major threats to the safe operation of civil infrastructures, so timely and accurate detection of cracks is crucial for accident prevention. However, in practical applications, the poor continuity and low contrast of many cracks (e.g., pavement cracks) pose a great challenge to image-based crack detection. In previous approaches, the detection results are often unsatisfactory due to the limitation of the dataset and the singularity of the detection method. To solve this problem, we propose a frequency and spatial dual guide network for crack detection. First, global frequency domain information is processed by the fast Fourier transform to extract low-level features, and then the convolutional neural network uses global frequency domain information to learn local features of the image to extract high-level features. Finally, the feature maps of the two are weighted and fused element by element to obtain the final detection result. Extensive experiments show that our method is superior to any other existing method.

Proceedings Article | 28 October 2021 Paper

A prediction method for population density in key areas

Zhihao Xu, Jianbo Li, Zhiqiang Lv, Yue Wang, Haoran Li

Proceedings Volume 11884, 1188403 (2021) https://doi.org/10.1117/12.2600837

Read Abstract +

Predicting the population density in certain key areas of the city is of great importance. It helps us rationally deploy urban resources, initiate regional emergency plans, reduce the spread risk of infectious diseases such as Covid-19, predict travel needs of individuals, and build intelligent cities. Although current researches focus on using the data of point-of-interest (POI) and clustering belonged to unsupervised learning to predict the population density of certain neighboring cities to define metropolitan areas, there is almost no discussion about using spatial-temporal models to predict the population density in certain key areas of a city without using actual regional images. We abstract 997 key areas in Beijing and their regional connections into a graph structure and propose a model called Word Embedded Spatial-temporal Graph Convolutional Network (WE-STGCN). WE-STGCN is mainly composed of three parts, which are the Spatial Convolution Layer, the Temporal Convolution Layer, and the Feature Component. Based on the data set provided by the Data Fountain platform, we evaluate the model and compare it with some typical models. Experimental results show that the Spatial Convolution Layer can merge features of the nodes and edges to reflect the spatial correlation, the Temporal Convolution Layer can extract the temporal dependence, and the Feature Component can enhance the importance of other attributes that affect the population density of the area. In general, the WE-STGCN is better than baselines and can complete the work of predicting population density in key areas.

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