Anfu Zhu

In visible–infrared object detection under complex environments, cross-modal fusion often suffers from spatial misalignment, semantic inconsistency, and unstable feature responses under varying illumination conditions. To address these issues, this paper proposes a dual-branch visible–infrared object detection framework based on YOLOv11. A staged refine–interact–modulate cross-modal fusion structure (RIFN) is designed to progressively stabilize the cross-modal fusion process. Specifically, the Cross-Modal Spatial–Channel Calibration (CSK) module first suppresses cross-modal discrepancies, the bidirectional Cross-Modal Synergy (CSM) module subsequently performs complementary interaction modeling, and the Complementary Modulation (CM) module further recalibrates fused representations to improve fusion stability under complex environments. Extensive experiments conducted on the public LLVIP and M³FD datasets demonstrate the effectiveness and generalization capability of the proposed framework. Compared with DEYOLO, the proposed method improves mAP@50 by 2.4% on the M³FD dataset while reducing GFLOPs by 2.4, demonstrating superior computational efficiency. In addition, compared with the Transformer-based GM-DETR, the proposed framework improves mAP@50 by 2.1% on the LLVIP dataset and increases inference speed by 210 FPS, achieving higher detection accuracy together with better inference efficiency. Experimental results demonstrate that the proposed staged cross-modal fusion strategy can effectively enhance the robustness of visible–infrared object detection under complex illumination conditions and background interference.

As a high-end abrasive material, diamond is prone to defects such as cracks and ablation during laser cutting due to complex physical interactions. Achieving efficient and accurate detection of laser-cut diamond defects is crucial for enhancing product quality and production efficiency. Traditional deep learning object detection algorithms generally face challenges like large parameter magnitudes and heavy computational burdens, making them difficult to apply in potential scenarios of resource-restricted equipment such as handheld inspection devices. In response to this problem, this research develops FAS-YOLO, a lightweight defect detection model for laser-cut diamonds based on the YOLOv11n framework. Initially, the FDConv (Frequency Domain Convolution) module is integrated to reconstruct the C3k2 feature extraction component, enhancing the capture of core defect features.Second, the ADown (Adaptive Downsampling) module is employed to refine the downsampling layer, resolving parameter redundancy. The SEAM attention mechanism is integrated to enhance the detection head module. By learning the importance of different channels and fusing channel information, the model is guided to focus more precisely on defect region features while suppressing background interference such as metal reflections. Experimental results demonstrate that the FAS-YOLO model achieves 92% precision, 80.4% recall, and an mAP50 value of 82.6%. Compared to the baseline architecture YOLOv11n, it achieves competitive performance while reducing the number of parameters, GFLOPS, and model size by 37.4%, 40%, and 34.6%, respectively.

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.

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%.

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.

In this work, we propose a novel dual optical frequency comb (DOFC) generation scheme based on dual cascaded difference frequency generation (DCDFG). Feasible designs are introduced that enable the two sets of cascaded optical waves, initially generated by DCDFG in an aperiodically periodically poled lithium niobate (APPLN) crystal with a pump wave and two signal waves, then transferred to high-order Stokes waves by oscillations of cascaded Stokes waves and the optimization of phase mismatching of each-order DCDFG; finally, a DOFC was constructed. We demonstrate a high-performance DOFC with characteristics of high repetition frequency difference, tunable repetition frequency difference, high flatness, and a tunable spectral distribution range by providing a theoretical framework. We argue that the scheme proposed in this work is promising for achieving a high-quality DOFC.

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.

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.

Выравнивание нестационарного канала подводной акустической системы связи OFDM

In this paper, three time-varying channel equalization schemes are studied in the underwater acoustic (UWA) Orthogonal Frequency Division Multiplexing (OFD