Automated pavement crack image segmentation presents a significant challenge due to the difficulty in detecting slender cracks on complex pavement backgrounds, as well as the significant impact of lighting conditions. In this paper, we propose a novel approach for automated pavement crack detection using a multi-scale feature fusion network based on the Transformer architecture, leveraging an encoding-decoding structure. In the encoding phase, the Transformer is leveraged as a substitute for the convolution operation, which utilizes global modeling to enhance feature extraction capabilities and address long-distance dependence. Then, dilated convolution is employed to increase the receptive field of the feature map while maintaining resolution, thereby further improving context information acquisition. In the decoding phase, the linear layer is employed to adjust the length of feature sequence output by different encoder block, and the multi-scale feature map is obtained after dimension conversion. Detailed information of cracks can be restored by fusing multi-scale features, thereby improving the accuracy of crack detection. Our proposed method achieves an F1 score of 70.84% on the Crack500 dataset and 84.50% on the DeepCrack dataset, which are improvements of 1.42% and 2.07% over the state-of-the-art method, respectively. The experimental results show that the proposed method has higher detection accuracy, better generalization and better crack detection results can be obtained under both high and low brightness conditions.

As a current popular method, intelligent detection of cracks is of great significance to road safety, so deep learning has gradually attracted attention in the field of crack image detection. The nonlinear structure, low contrast and discontinuity of cracks bring great challenges to existing crack detection methods based on deep learning. Therefore, an end-to-end deep convolutional neural network (AttentionCrack) is proposed for automatic crack detection to overcome the inaccuracy of boundary location between crack and non-crack pixels. The AttentionCrack network is built on U-Net based encoder-decoder architecture, and an attention mechanism is incorporated into the multi-scale convolutional feature to enhance the recognition of crack region. Additionally, a dilated convolution module is introduced in the encoder-decoder architecture to reduce the loss of crack detail due to the pooling operation in the encoder network. Furthermore, since up-sampling will lead to the loss of crack boundary information in the decoder network, a depthwise separable residual module is proposed to capture the boundary information of pavement crack. The AttentionCrack net on public pavement crack image datasets named CrackSegNet and Crack500 is trained and tested, the results demonstrate that the AttentionCrack achieves F1 score over 0.70 on the CrackSegNet and 0.71 on the Crack500 in average and outperforms the current state-of-the-art methods.

BIM is one of the main technical ways to realize building informatization, and the model’s texture is essential to its style design during BIM construction. However, the texture maps provided by mainstream BIM software are not realistic enough and monotonous to meet the actual needs of users for the model style. Therefore, an interior furniture BIM style restoration method was proposed based on image retrieval and object location using convolutional neural network. First, two types of furniture images, namely grayscale contour images from BIM software and real images from the Internet, were collected to train the following network model. Second, a multi-feature weighted fusion neural network model based on an attention mechanism (AM-rVGG) was proposed, which focused on the structural information of furniture images to retrieve the most similar real image, and then some furniture image patches from the retrieved one were generated with object location and random cropping techniques as the candidate texture maps of the furniture BIM. Finally, the candidate ones were fed back into the BIM software to realize the restoration of the furniture BIM style. The experimental results showed that the average retrieval accuracy of the proposed network model was 83.1%, and the obtained texture maps could effectively restore the real style of the furniture BIM. This work provides a new idea for restoring the realism in other BIM.