Improved U-net network asphalt pavement crack detection method.

Road crack detection is one of the important parts of road safety detection. Aiming at the problems such as weak segmentation effect of basic U-Net on pavement crack, insufficient precision of crack contour segmentation, difficult to identify narrow crack and low segmentation accuracy, this paper proposes an improved U-net network pavement crack segmentation method. VGG16 and Up_Conv (Upsampling Convolution) modules are introduced as backbone network and feature enhancement network respectively, and the more abstract features in the image are extracted by using the Block depth separable convolution blocks, and the multi-scale features are captured and enhanced by higher level semantic information to improve the recognition accuracy of narrow cracks in the road surface. The improved network embedded the Ca(Channel Attention) attention mechanism in U-net's jump connection to enhance the crack portion to suppress background noise. At the same time, DG_Conv(Depthwise GSConv Convolution) module and UnetUp(Unet Upsampling) module are added in the decoding part to extract richer features through more convolutional layers in the network, so that the model pays more attention to the detailed part of the crack, so the segmentation accuracy can be improved. In order to verify the model's ability to detect cracks in complex backgrounds, experiments were carried out on CFD and Deepcrack datasets. The experimental results show that compared with the traditional U-net network F1-score and mIoU have increased by 13.6% and 9.9% respectively. Superior to advanced models such as U-net, Segnet and Linknet in accuracy and generalization ability, the improved model provides a new method for asphalt pavement crack detection. The model is more conducive to practical application and ground deployment, and can be applied in road maintenance projects.

Mirror-symmetric double-negative metamaterial resonator with polarization insensitivity and tunable sandwiched structure for multiband wireless communications.

This paper presents a novel Double-negative (DNG) metamaterial (MM) resonator with a mirror-symmetric configuration, designed to exhibit multiband resonances in the S, C, and X bands. The resonator is fabricated using advanced processing techniques on a Rogers 5880 substrate and features electrodeposited copper. It consists of four equal regions, each containing interconnected split-ring resonators that are connected through a cross-shaped structure to ensure mirror symmetry. The simulation results demonstrate six resonance points at frequencies of 2.45 GHz, 4.27 GHz, 6.86 GHz, 8.98 GHz, 10.69 GHz, and 11.65 GHz. These resonances are characterized by near-zero/negative permeability, negative permittivity, refractive index, and impedance. Furthermore, the cross-polarization effect of incident waves is investigated. Additionally, the potential for tunability of resonance frequencies is explored through a sandwiched configuration of the MM resonator, achieved by modifying the cover of the resonating patch. Moreover, the equivalent circuit model of the proposed MM resonator is in good agreement with practical measurements, validating the simulation results. The new tuning method for MM resonators holds the promise for future sensing applications and wireless communications.