No abstract

To solve the problem caused by jamming, an acousto-optic tunable filter (AOTF)-based imaging spectrometer and a corresponding spatial–spectral discrimination method are proposed for aerial targets. The system has the capability of staring imaging and is electronically tunable, which provides the spatial location and a distinguishable spectral feature in a few images. Since AOTF operates in a frame mode, the spectral brightness of the targets can be predicted by Kalman filtering, like with the motion model. The final target state is updated by using synthetic spatial–spectral information to realize fast decision-making. The results show that the proposed method is more targeted to solve the problem caused by jamming, compared with the traditional energy discrimination method.

In the production process of metal castings, certain defects are easy to appear on the surface. Before the castings are put into use, it is necessary to detect whether there are defects on the surface. In this paper, the defect detection of metal casting surface is taken as the research object, and the Centernet deep learning model is used to recognize the defect of casting surface image. Centernet does not use Region Proposal Network (RPN) and Non-Maximum Suppression (NMS) in the training process. It has the advantages of simple model, good detection effect, fast running speed and easy integration and deployment. By predicting the category heat map, the length and width map of the detection frame and the center offset of the detection frame with the same image size as the input model, the defect target can be extracted. In this paper, the image dataset is expanded by cropping, rotating, changing brightness and contrast of the images. For the pit and scratch defects of metal castings, the Mean Average Precision(mAP) is higher than 0.9 for the metal casting defect image dataset, and the software integration of the model is completed.

The atmospheric temperature and humidity profiles of the troposphere are generally measured by radiosondes and satellites, which are essential for analyzing and predicting weather. Nevertheless, the insufficient observation frequencies and low detection accuracy of the boundary layer restricts the description of atmospheric state changes by the temperature and humidity profiles. Therefore, this work focus on retrieving the temperature and humidity profiles using observations of the FengYun-4 (FY-4) Geostationary Interferometric Infrared Sounder (GIIRS) combined with ground-based infrared spectral observations from the Atmospheric Emitted Radiance Interferometer (AERI), which are more accurate than space-based individual retrieval results and have a wider effective retrieval range than ground-based individual retrieval results. Based on the synergistic observations, which are made by matching the space-based and ground-based data with those of different spatial and temporal resolutions, a synergistic retrieval process is proposed to obtain the temperature and humidity profiles at a high frequency under clear-sky conditions based on the optimal estimation method. In this research, using the line-by-line radiative transfer model (LBLRTM) as the forward model for observing simulations, a retrieval experiment was carried out in Qingdao, China, where an AERI is situated. Taking radiosonde data as a reference for comparing the retrieval results of the temperature and humidity profiles of the troposphere, the root-mean-square error (RMSE) of the synergistic retrieval algorithm below 400 hPa is within 2 K for temperature and within 12% for relative humidity. Compared with the GIIRS individual retrieval, the RMSE of temperature and relative humidity for the synergistic method is reduced by 0.13–1.5 K and 2.7–4.4% at 500 hPa, and 0.13–2.1 K and 2.5–7.2% at 900 hPa. Moreover, the forecast index (FI) calculated from the retrieval results shows reasonable consistency with the FIs calculated from the ERA5 reanalysis and from radiosonde data. The synergistic retrieval results have higher temporal resolution than space-based retrieval results and can reflect the changes in the atmospheric state more accurately. Overall, the results demonstrated the promising potential of the synergistic retrieval of temperature and humidity profiles at high accuracy and high temporal resolution under clear-sky conditions from FY-4/GIIRS and AERI.

Fringe projection profilometry is an efficient, fast and non-contact 3D measurement technique, widely used in industrial parts measurement. However, invalid phases are common when measuring step edges such as holes, ribs and steps in industrial parts which leads to outliers in reconstructed point cloud and ultimately causes reduction of valid point cloud and dimensional measurement errors. In this paper, an error compensation method on 3D measurement of step edge is proposed. 3D measurement space is firstly divided into multiple subspaces based on binocular camera system parameters. Then a calibration method is proposed to calculate the amount of compensation for each subspace and obtain parameters of edge error model. After calibration, point cloud of object with step edge is reconstructed by projecting phase-shifting structured light fringe patterns. By using Principal Component Analysis (PCA), normal estimation of point cloud is implemented. And edge feature is extracted with combination of eigenvalues variation of the covariance matrix and first-and second-order fitting based on two-dimensional projection. At the same time, the corresponding deviation amount of each edge point is solved on the basis of aforementioned edge error model. Finally, the accurate step edge is obtained after error compensation according to the normal direction and deviation amount. Experimental results show that, the method proposed can effectively improve the 3D measurement accuracy of step edge.