In the daily inspection task of the expressway, accuracy and speed are the two most important indexes to reflect the detection efficiency of nondeformation diseases of asphalt pavement. To achieve model compression, accelerated detection, and accurate identification under multiscale conditions, a lightweight algorithm (PDNet) based on improved YOLOv5 is proposed. The algorithm is improved based on the network structure of YOLOv5, and the improved network structure is called YOLO-W. Firstly, a novel cross-layer weighted cascade aggregation network (W-PAN) is proposed to replace the original YOLOv5 network. Secondly, more economical GhostC3 and ShuffleConv modules are designed to replace C3 and Conv modules in the original network model. In terms of parameter setting, CIoU is selected as the loss function of the model, and the K-Means ++ algorithm is used for anchor box clustering. Before the model training, the confrontation generation network (GAN) and Poisson migration fusion algorithm (Poisson) are used for data enhancement and the negative sample training (NST) method is used to improve the robustness of the model. Finally, Softer-NMS is used to remove the prediction box in the prediction stage. Seven common asphalt pavement disease data sets (FAFU-PD) are constructed at the same time. Compared with the original YOLOv5 algorithm, PDNet improves the scores of FAFU-PD data sets on F1-score by 10 percentage points and FPS by 77.5%.

With the ever-increasing demand for marine products with high nutritional value in the sea, rapid and accurate detection of target organisms is highly desired. In this context, a novel underwater object detection algorithm based on improved Single Shot MultiBox Detector (SSD) is proposed to improve the detection accuracy and real-time performance in complex underwater environment. The proposed algorithm employs ResNeXt-50 instead of VGGNet-16 as the backbone network to improve the ability to obtain details and position information of the target object. To be more specific, a channel-spatial attention mechanism is incorporated in the deep layers of the improved XC-SSD model to enhance semantic information of high-level feature maps and reduce missed detection rate and false detection rate. Comprehensive experiments on the URPC-2019 dataset show that the detection accuracy and speed of the proposed algorithm are 79.76% and 18.95 FPS, respectively, which are 3.49% and 2.8 FPS higher than the original SSD algorithm. Experimental results clearly demonstrate that the proposed algorithm is highly efficient and suitable for real-time object detection.

The emerging 1mm resolution 3D data collection technology is capable of covering the entire pavement surface, and provides more data sets than traditional line-of-sight data collection systems. As a result, quantifying the impact of sample size including sample width and sample length on the calculation of pavement texture indicators is becoming possible. In this study, 1mm 3D texture data are collected and processed at seven test sites using the PaveVision3D Ultra system. Analysis of Variance (ANOVA) test and linear regression models are developed to investigate various sample length and width on the calculation of three widely used texture indicators: Mean Profile Depth (MPD), Mean Texture Depth (MTD) and Power Spectra Density (PSD). Since the current ASTM standards and other procedures cannot be directly applied to 3D surface for production due to a lack of definitions, the results from this research are beneficial in the process to standardize texture indicators’ computations with 1mm 3D surface data of pavements.

Molecular dynamics simulations of polyethylene/silver-nanoparticle composites are implemented to calculate the structures, electrical, thermal and mechanical properties, thereby investigating their relationships with the nanoparticle dimension and simulation temperature. The results show that polyethylene/silver-nanoparticle composites are of isotropic amorphous structure, and the dispersion of nanoparticles in composite can be enhanced at a relatively higher temperature. Multi-layers of atoms on nanoparticle surface change into amorphous configurations, and electrical polarization interface layers are formed between silver nanoparticles and polyethylene matrix. The interface region shrinks and expends respectively with nanoparticle dimension and temperature increasing. Compared with polyethylene system, the polyethylene/silver-nanoparticle composite presents explicitly high polarizability which increases with temperature and nanoparticle size rising simultaneously. The silver nanopaticle dimension directly influences the intensity and frequency of interfacial dipole moment, resulting in corresponding variations of peak position and intensity in infrared spectrum. The polyethylene/silver-nanoparticle composite also shows higher isometric heat capacity and negative thermal pressure coefficient with better temperature stability, which decreases explicitly with temperature and nanoparticle size increasing respectively, than polyethylene system. The mechanical property of polyethylene/silver-nanoparticle composite shows isotropic elastic constant tensor with considerably higher Young modulus and Poisson ratio than the polyethylene system, both of which decrease with temperature and nanoparticle dimension increasing, which indicates the improvement on mechanical property with Ag nanoparticle filler.

This paper designs a system platform for graphical programming. The system is based on the open source software platform developed for the second time, which can realize the layer-by-layer conversion of diagrams and source codes, allowing users to deeply understand the logical relationship of programming, and programming and the host can be downloaded. The user can complete the drag-and-drop procedure, the icon shows the basic elements of the function, and the attribute box can modify each variable and automatically build it. After the user builds a graphics program with this system, click the run button to generate Python code and save the project.