Surface-enhanced Raman spectroscopy (SERS) is a molecule-specific spectroscopic technique known for its high sensitivity, rapid response, and non-destructive testing capabilities. The physical structure of the SERS substrate has the most significant impact on the enhancement effect, which has nanoscale roughness or specific metal nanostructures, such as metal nanoparticles, nanowires, or nanorods. Upon exposure to laser irradiation, nanostructures induce an intensified electromagnetic field, namely localized surface plasmon resonance (LSPR), thereby augmenting the Raman scattering signal. Furthermore, the spatial arrangement of metal nanostructures similarly influences the LSPR. In this study, we designed a three-dimensional porous SERS substrate that has a larger surface area than traditional planar substrates, adsorbing more silver nanoparticles (Ag NPs). We utilize the aggregation effect of Ag NPs to induce an enhancement of the electromagnetic field, thereby amplifying the SERS signal. To validate the efficacy of the porous SERS substrate, this study employs Comsol simulation software to compare the electromagnetic field intensity between the porous substrate and conventional planar structures. Simulation results demostrate that the porous structure achieves a stronger electric field intensity under the same laser irradiation, thus enhancing the SERS signal intensity.
Detecting pavement cracks from images is a complex computer vision task due to their varying shapes, backgrounds, and sizes. We propose CrackF-Net, an end-to-end convolutional neural network for automatic crack detection in road images. We construct the CrackF-Net network using an encoder–decoder architecture to extract image features in convolutional blocks with residuals and fuse the multiscale convolutional features produced by the decoder. Convolutional blocks with residuals are used to capture the strong semantic features of cracks, and an adaptive filter fusion module is proposed to assist the network make a selection of filter fusion features on the channels. CrackF-Net fuses the multiscale features in decoder to improve crack detection performance. The proposed CrackF-Net is compared to other advanced crack detection methods using three public datasets. The experimental results show that CrackF-Net achieves state-of-the-art performance, which obtains F-measures of 0.866, 0.737, and 0.852 on the three datasets.
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