Furthermore, two promising research fields, namely construction robots and construction cybersecurity, are discussed as future opportunities. The main research topics and objects are summarized for each research area.
Three main research areas, namely data mining for intelligence, digitalization, and automation, are identified. To figure out the current state and future opportunities of data mining for Construction 4.0, this study conducts a bibliometric analysis with three steps: (1) determining research context and scope, (2) retrieving literature from Web of Science, and (3) modeling and visualizing the word similarity network using VOSviewer. Currently, there is no summary of data mining applications in terms of Construction 4.0. The powerful ability of data mining to process and utilize data makes it the preferred option for solving data-related problems. In the context of Construction 4.0, the data-intensive nature of the AEC/FM industry puts data-related issues at its core. The video taken in real-time experiments is released at.
PAZ ANALYSER CRACK CRACK
The developed system can segment pavement crack in real-time on an embedded device Jetson TX2 (25 FPS). Additionally, a light-weighted version of the model generated by introducing depthwise separable convolution achieves better a performance and a much faster processing speed with 1/30 of the number of U-Net parameters. Compared with other state-of-the-art networks, the proposed model achieves better performance and the functionalities of adding residual blocks and hybrid attention mechanisms are validated in a comprehensive ablation study. An image data set containing 789 pavement crack images collected by a self-designed mobile robot is constructed and used for training and evaluating the proposed model. The hybrid attention blocks are designed to fuse both low-level features and high-level features to help the model focus on correct channels and areas of cracks, thereby improving the feature presentation ability of RHA-Net. The ResBlocks are used to improve the ability of RHA-Net to extract high-level abstract features. The RHA-Net is built by integrating residual blocks (ResBlocks) and hybrid attention blocks into the encoder-decoder architecture.
In this paper, an efficient and effective end-to-end network for automatic pavement crack segmentation, called RHA-Net, is proposed to improve the pavement crack segmentation accuracy.
The acquisition and evaluation of pavement surface data play an essential role in pavement condition evaluation. Experimental results prove the state-of-the-art detection precision of CrackForest compared with competing methods.
In addition, our method is faster and easier to parallel. Our contributions are shown as follows: 1) apply the integral channel features to redefine the tokens that constitute a crack and get better representation of the cracks with intensity inhomogeneity 2) introduce random structured forests to generate a high-performance crack detector, which can identify arbitrarily complex cracks and 3) propose a new crack descriptor to characterize cracks and discern them from noises effectively. In this paper, we propose CrackForest, a novel road crack detection framework based on random structured forests, to address these issues. However, as the key part of an intelligent transportation system, automatic road crack detection has been challenged because of the intense inhomogeneity along the cracks, the topology complexity of cracks, the inference of noises with similar texture to the cracks, and so on. Cracks are a growing threat to road conditions and have drawn much attention to the construction of intelligent transportation systems.
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