Tunnel linings require routine inspection as they have a big impact on a tunnel’s safety and longevity. In this study, the convolutional neural network was utilized to develop the MFF-YOLO model. To improve feature learning efficiency, a multi-scale feature fusion network was constructed within the neck network. Additionally, a reweighted screening method was devised at the prediction stage to address the problem of duplicate detection frames. Moreover, the loss function was adjusted to maximize the effectiveness of model training and improve its overall performance. The results show that the model has a recall and accuracy that are 7.1% and 6.0% greater than those of the YOLOv5 model, reaching 89.5% and 89.4%, respectively, as well as the ability to reliably identify targets that the previous model error detection and miss detection. The MFF-YOLO model improves tunnel lining detection performance generally.

The defect identification of tunnel lining is a task with a lot of tasks and time‐consuming work, and currently, it mainly relies on manual operation. This paper takes the ground‐penetrating radar image of the internal defects of the lining as the research object, and chooses the popular VGG16, ResNet34 convolutional neural network (CNN) to build the automatic recognition model for comparative study, and proposes an improved ResNet34 defect‐recognition model. In this paper, SGD and Adam training algorithms are used to update network parameters, and the PyTorch depth framework is used to train the network. The test results show that the ResNet34 network has faster convergence speed, higher accuracy rate, and shorter training time than the VGG16 network. The ResNet34 network using the Adam algorithm can achieve 99.08% accuracy. The improved ResNet34 network can achieve an accuracy of 99.25%, and at the same, reduce the parameter amount by 4.22% compared with the ResNet34 network, which can better identify defects in the lining. The research in this paper shows that the deep learning method can provide new ideas for the identification of tunnel lining defects.

One of the greatest engineering feats in history is the construction of tunnels, and the management of tunnel safety depends heavily on the detection of tunnel defects. However, the real-time, portability, and accuracy issues with the present tunnel defect detection technique still exist. The study improves the traditional defect detection technology based on the knowledge distillation algorithm, the depth pooling residual structure is designed in the teacher network to enhance the ability to extract target features. Next, the MobileNetv3 lightweight network is built into the student network to reduce the number and volume of model parameters. The lightweight model is then trained in terms of both features and outputs using a multidimensional knowledge distillation approach. By processing the tunnel radar detection photos, the dataset is created. The experimental findings demonstrate that the multidimensional knowledge distillation approach greatly increases the detection efficiency: the number of parameters is decreased by 81.4%, from 16.03 MB to 2.98 MB, while the accuracy is improved by 2.5%, from 83.4% to 85.9%.

The problem of solving regulator-conjugate matrix equations is considered in this paper. The regulator-conjugate matrix equations are a class of nonhomogeneous equations. Utilizing several complex matrix operations and the concepts of controllability-like and observability-like matrices, a special solution to this problem is constructed, which includes solving an ordinary algebraic matrix. Combined with our recent results on Sylvester-conjugate matrix equations, complete solutions to regulator-conjugate matrix equations can be obtained by superposition principle. The correctness and effectiveness are verified by a numerical example.

Aiming at the problem of insufficient number of samples due to the difficulty of data acquisition in the identification of tunnel lining defects, a generative adversarial network was introduced to expand the data, and the network was improved for the mode collapse problem of the traditional generative adversarial network and the problem that the generated image features were not obvious. On the basis of the WGAN-GP network, a deep convolutional network is selected as its backbone network, and the effectiveness of the deep convolutional network in feature extraction by Lv et al. (2022) is used to improve the quality of the images generated by the network. In addition, the residual module is introduced into the discriminator network, and the upsampling module is introduced into the generator network, which further solves the problem of gradient disappearance of the two networks during the update iteration process through the idea of cross-connection, while better retaining the underlying features, which effectively solves the problem of mode collapse and low quality of generated images in the generative adversarial network. Compared with the original network, the image quality of the generated adversarial network is improved, and the discriminator and generator losses converge faster. At the same time, the recognition accuracy of the YOLOv5 network is improved by 4.4 and the overfitting phenomenon is alleviated, which proves the effectiveness of the method under the limited training data set.

Due to the polycrystalline cubic boron nitride tool has the characteristics of high hardness, brittleness, etc., it is easy to break the tool or produce defects in the laser cutting process, which affects the cutting performance of the tool. Traditional defect detection methods can no longer meet the needs of modern manufacturing. Aiming at the problems of low accuracy and poor real-time detection of surface defects on laser-cutting polycrystalline cubic boron nitride tools, this study proposes the surface defect detection model of laser cutting polycrystalline cubic boron nitride tool based on asymptotic fusion strategy, which fills the gap in the field. In the backbone network, the C3SE module is constructed by modeling the correlation between feature channels to improve the model’s focus on key features in order to enhance the feature extraction and processing capability of the backbone network; In the neck network, adaptive spatial fusion operation and direct interaction of non-adjacent layers are utilized for multi-scale information fusion, and the asymptotic feature pyramid network for object detection (AFPN) is used instead of the FPN structure to improve the detection performance; In the head network, a soft suppression mechanism is introduced to reduce the overlapping frame score using a decay function, thus improving the detection accuracy. The experimental results based on the self-constructed laser-cutting polycrystalline cubic boron nitride tool surface defects dataset show that the average accuracy of the AFFS-YOLO model is improved by 5.6% compared with that of the YOLOv5 model, reaching 86.1%, and the detection effect is better than that of the original network and other classical target detection networks.