Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding

Crack is one of the most common road distresses which may pose road safety hazards. Generally, crack detection is performed by either certified inspectors or structural engineers. This task is, however, time-consuming, subjective and labor-intensive. In this paper, we propose a novel road crack detection algorithm based on deep learning and adaptive image segmentation. Firstly, a deep convolutional neural network is trained to determine whether an image contains cracks or not. The images containing cracks are then smoothed using bilateral filtering, which greatly minimizes the number of noisy pixels. Finally, we utilize an adaptive thresholding method to extract the cracks from road surface. The experimental results illustrate that our network can classify images with an accuracy of 99.92%, and the cracks can be successfully extracted from the images using our proposed thresholding algorithm.Comment: 6 pages, 8 figures, 2019 IEEE Intelligent Vehicles Symposiu

Study on the Mechanical Properties and Damage Constitutive Model of Hybrid Fibre- Reinforced EPS Lightweight Aggregate Concrete

The mechanical properties of hybrid fibre (polypropylene and wood)-reinforced expanded polystyrene (EPS) lightweight aggregate concrete was studied under various sand contents. Cubic and prismatic compression tests were carried out, through which the basic mechanical properties and stress-strain curves of the specimens were obtained. Moreover, the microstructures of the fibre-reinforced concrete with different sand proportions were analysed via scanning electron microscopy (SEM). The test results showed that as the sand proportion increased, the cubic compressive strength and prismatic compressive strength of the EPS lightweight aggregate concrete increased. The optimal slump was obtained when the sand ratio was 25%, after which the slump declined. The EPS lightweight aggregate concrete exhibited obvious elastoplastic behaviour during compression, and the corresponding stress-strain curve could be divided into four stages: the elastic stage, strengthening stage, softening stage and collapse platform stage. Moreover, based on the test results and the damage theory and considering the coupling relationship between plasticity and damage, a damage constitutive model was proposed for hybrid fibre-reinforced EPS lightweight aggregate concrete under uniaxial compression

Med-Tuning: Exploring Parameter-Efficient Transfer Learning for Medical Volumetric Segmentation

Deep learning based medical volumetric segmentation methods either train the model from scratch or follow the standard "pre-training then finetuning" paradigm. Although finetuning a well pre-trained model on downstream tasks can harness its representation power, the standard full finetuning is costly in terms of computation and memory footprint. In this paper, we present the first study on parameter-efficient transfer learning for medical volumetric segmentation and propose a novel framework named Med-Tuning based on intra-stage feature enhancement and inter-stage feature interaction. Given a large-scale pre-trained model on 2D natural images, our method can exploit both the multi-scale spatial feature representations and temporal correlations along image slices, which are crucial for accurate medical volumetric segmentation. Extensive experiments on three benchmark datasets (including CT and MRI) show that our method can achieve better results than previous state-of-the-art parameter-efficient transfer learning methods and full finetuning for the segmentation task, with much less tuned parameter costs. Compared to full finetuning, our method reduces the finetuned model parameters by up to 4x, with even better segmentation performance

Diffraction-Free Bloch Surface Waves

In this letter, we demonstrate a novel diffraction-free Bloch surface wave (DF-BSW) sustained on all-dielectric multilayers that does not diffract after being passed through three obstacles or across a single mode fiber. It can propagate in a straight line for distances longer than 110 {\mu}m at a wavelength of 633 nm and could be applied as an in-plane optical virtual probe, both in air and in an aqueous environment. The ability to be used in water, its long diffraction-free distance, and its tolerance to multiple obstacles make this DF-BSW ideal for certain applications in areas such as the biological sciences, where many measurements are made on glass surfaces or for which an aqueous environment is required, and for high-speed interconnections between chips, where low loss is necessary. Specifically, the DF-BSW on the dielectric multilayer can be used to develop novel flow cytometry that is based on the surface wave, but not the free space beam, to detect the surface-bound targets

FreMAE: Fourier Transform Meets Masked Autoencoders for Medical Image Segmentation

The research community has witnessed the powerful potential of self-supervised Masked Image Modeling (MIM), which enables the models capable of learning visual representation from unlabeled data. In this paper, to incorporate both the crucial global structural information and local details for dense prediction tasks, we alter the perspective to the frequency domain and present a new MIM-based framework named FreMAE for self-supervised pre-training for medical image segmentation. Based on the observations that the detailed structural information mainly lies in the high-frequency components and the high-level semantics are abundant in the low-frequency counterparts, we further incorporate multi-stage supervision to guide the representation learning during the pre-training phase. Extensive experiments on three benchmark datasets show the superior advantage of our proposed FreMAE over previous state-of-the-art MIM methods. Compared with various baselines trained from scratch, our FreMAE could consistently bring considerable improvements to the model performance. To the best our knowledge, this is the first attempt towards MIM with Fourier Transform in medical image segmentation

UAV hyperspectral analysis of secondary salinization in arid oasis cotton fields: effects of FOD feature selection and SOA-RF

Secondary salinization is a crucial constraint on agricultural progress in arid regions. The specific mulching irrigation technique not only exacerbates secondary salinization but also complicates field-scale soil salinity monitoring. UAV hyperspectral remote sensing offers a monitoring method that is high-precision, high-efficiency, and short-cycle. In this study, UAV hyperspectral images were used to derive one-dimensional, textural, and three-dimensional feature variables using Competitive adaptive reweighted sampling (CARS), Gray-Level Co-occurrence Matrix (GLCM), Boruta Feature Selection (Boruta), and Brightness-Color-Index (BCI) with Fractional-order differentiation (FOD) processing. Additionally, three modeling strategies were developed (Strategy 1 involves constructing the model solely with the 20 single-band variable inputs screened by the CARS algorithm. In Strategy 2, 25 texture features augment Strategy 1, resulting in 45 feature variables for model construction. Strategy 3, building upon Strategy 2, incorporates six triple-band indices, totaling 51 variables used in the model’s construction) and integrated with the Seagull Optimization Algorithm for Random Forest (SOA-RF) models to predict soil electrical conductivity (EC) and delineate spatial distribution. The results demonstrated that fractional order differentiation highlights spectral features in noisy spectra, and different orders of differentiation reveal different hidden information. The correlation between soil EC and spectra varies with the order. 1.9th order differentiation is proved to be the best order for constructing one-dimensional indices; although the addition of texture features slightly improves the accuracy of the model, the integration of the three-waveband indices significantly improves the accuracy of the estimation, with an R2 of 0.9476. In contrast to the conventional RF model, the SOA-RF algorithm optimizes its parameters thereby significantly improving the accuracy and model stability. The optimal soil salinity prediction model proposed in this study can accurately, non-invasively and rapidly identify excessive salt accumulation in drip irrigation under membrane. It is of great significance to improve the growing conditions of cotton, increase the cotton yield, and promote the sustainable development of Xinjiang’s agricultural economy, and also provides a reference for the prevention and control of regional soil salinization

Ethanol Steam Reforming over Ni/ZSM-5 Nanosheet for Hydrogen Production

Compared to reforming reactions using hydrocarbons, ethanol steam reforming (ESR) is a sustainable alternative for hydrogen (H2) production since ethanol can be produced sustainably using biomass. This work explores the catalyst design strategies for preparing the Ni supported on ZSM-5 zeolite catalysts to promote ESR, specifically, two-dimensional ZSM-5 nanosheet and conventional ZSM-5 crystal were used as the catalyst carriers and two synthesis strategies, i.e., in situ encapsulation and wet impregnation method, were employed to prepare the catalysts. Based on the comparative characterization of the catalysts and comparative catalytic assessments, it was found that the combination of the in situ encapsulation synthesis and the ZSM-5 nanosheet carrier was the effective strategy to develop catalysts for promoting H2 production via ESR due to the improved mass transfer (through the 2-D structure of ZSM-5 nanosheet) and formation of confined small Ni nanoparticles (resulted via the in situ encapsulation synthesis). In addition, the resulting ZSM-5 nanosheet supported Ni catalyst also showed high Ni dispersion and high accessibility to Ni sites by the reactants, being able to improve the activity and stability of catalysts and suppress metal sintering and coking during ESR at high reaction temperatures. Thus, the Ni supported on ZSM-5 nanosheet catalyst prepared by encapsulation showed the stable performance with ~88% ethanol conversion and ~65% H2 yield achieved during a 48-h longevity test at 550 °C. Keywords: ZSM-5 nanosheet; In situ encapsulation; Ni catalyst; Ethanol steam reforming of (ESR); Hydrogen (H2) production