Salient object detection aims to distinguish the most visually conspicuous regions, playing an important role in computer vision tasks. However, complex natural scenarios can challenge salient object detection, hindering accurate extraction of objects with rich morphological diversity. This paper proposes a novel method for salient object detection leveraging multi-visual perception, mirroring the human visual system’s rapid identification, and focusing on impressive objects/regions within complex scenes. First, a feature map is derived from the original image. Then, salient object detection results are obtained for each perception feature and combined via a feature fusion strategy to produce a saliency map. Finally, superpixel segmentation is employed for precise salient object extraction, removing interference areas. This multi-feature approach for salient object detection harnesses complementary features to adapt to complex scenarios. Competitive experiments on the MSRA10K and ECSSD datasets place our method in the first tier, achieving 0.1302 MAE and 0.9382 F-measure for the MSRA10K dataset and 0.0783 MAE and and 0.9635 F-measure for the ECSSD dataset, demonstrating superior salient object detection performance in complex natural scenarios.

A simultaneous surface pressure and displacement measurement method that integrates pressure-sensitive paint (PSP) and binocular stereophotogrammetry is proposed. The assays were completed on the Φ4 m rotor test stand at China Aerodynamic Research and Development Center (CARDC). A single-shot lifetime approach was utilized to acquire the instantaneous pressure field on a rotor blade coated with PSP. At the same time, the PSP feature points were used to obtain the 3D coordinates of stereo cameras, which yielded the blade displacement field. The experimental results showed that the displacement measuring accuracy was better than 0.2 mm, and the pressure measurement accuracy was not affected, with Standard Deviation (STD) values below 700 Pa. The advantages of the proposed system are its simple structure, low cost, high accuracy and high test efficiency, which will offer a practical solution for the exploration of fluid–structure interplay. Hence, such a system is a prospective for the wind tunnel tests of helicopter rotor blades.

To address the issue of blurred images generated during ice wind tunnel tests, we propose a high-resolution dense-connection GAN model, named Dense-HR-GAN. This issue is caused by attenuation due to scattering and absorption when light passes through cloud and fog droplets. Dense-HR-GAN is specifically designed for this environment. The model utilizes an atmospheric scattering model to dehaze images with a dense network structure for training. First, sub-pixel convolution is added to the network structure to remove image artifacts and generate high-resolution images. Secondly, we introduce instance normalization to eliminate the influence of batch size on the model and improve its generalization performance. Finally, PatchGAN is used in the discriminator to capture image details and local information, and then drive the generator to generate a clear and high-resolution dehazed image. Moreover, the model is jointly constrained by multiple loss functions during training to restore the texture information of the hazy image and reduce color distortion. Experimental results show that the proposed method can achieve the state-of-the-art performance on image dehazing the in icing wind tunnel environment.

As an important part of the helicopter, the rotor directly affects flight safety and flight quality. Knowledge of the rotor dynamic behaviors is significant for validating and optimizing the performance of the helicopter rotor system. In this study, a photogrammetric system, based on 3D point tracking and stereo photogrammetry technology, is presented to solve the full-field dynamic motion and deformation parameters of rotating blades by identifying the retro-reflective targets arranged on the rotor. The photogrammetric system is demonstrated in the wind tunnel tests of a 2 m-diameter model rotor, conducted at the 5.5 m × 4 m Aeroacoustic Wind Tunnel of the China Aerodynamics Research and Development Center (CARDC). With the targets attached on the special hat installed directly over the rotor hub, a unified rotor coordinate system, that was stationary with respect to the rotor, could be established at any measuring instant. Therefore, by transforming the 3D coordinates of all measured targets to the rotor coordinate system, the blade displacements and deformations at different test conditions could be calculated consistently. Experimental results from current study were compared to simulation results calculated by the comprehensive analytical model of rotorcraft aerodynamics and dynamics (CAMRAD), which shows quite good agreements.

The characterization of pressure-sensitive paint (PSP) is affected by many physical and chemical factors, making it is difficult to analyze the relationship between characterization and influencing factors. An artificial neural network (ANN)-based method for predicting pressure sensitivity using paint thickness and roughness was proposed in this paper. The mean absolute percentage error (MAPE) for predicting pressure sensitivity is 6.5088%. The difference of paint thickness and roughness between sample and model surface may be a source of experimental error in PSP pressure measurement tests. The Stern-Volmer coefficients A and B are strongly linked. Pressure sensitivity is approximately equal to coefficient B, so coefficient A is predicted using pressure sensitivity based on the same ANN, and the MAPE of predicting A is 2.1315%. Then, we try to calculate the pressure by using the thickness and roughness on a model to predict pressure sensitivity and Stern-Volmer coefficient A. The PSP pressure measurement test was carried out at the China Aerodynamic Research and Development Center. Using the Stern-Volmer coefficient calculated by the in situ method, the method in this paper, and the sample calibration experiment, the root mean square errors (RMSE) of the pressure are 47.4431 Pa, 63.4736 Pa, and 73.0223 Pa, respectively.

In three-dimensional (3D) shape measurement techniques using structured light, 1-bit pulse-width modulation (PWM) patterns and 1-bit speckle patterns can be projected at high speed. However, when combining PWM and speckle patterns to integrate their advantages, the decoupling problem is insurmountable. In this work, a novel 1-bit speckle-embedded PWM (SPPWM) method was proposed to achieve absolute 3D shape measurement using only three binary patterns. Our method consists of three main steps: First, a sinusoidal pattern reconstruction network was proposed to eliminate the high-order harmonics and speckle patterns in the SPPWM patterns and obtain high-quality sinusoidal patterns. Second, a multi-temporal spatial correlation matching algorithm was proposed to obtain a coarse disparity map from the three SPPWM patterns. Third, the high-accuracy wrapped phase map is used as an additional constraint for refining the coarse disparity map to obtain the final high-accuracy disparity map for absolute 3D measurement without phase unwrapping. Our method combines the advantages of fringe projection profilometry techniques for high-precision wrapped phase retrieval and speckle correlation matching algorithms for robust and unambiguous disparity map calculation. The experimental results demonstrated that our method could realize high-precision absolute 3D shape measurement with an accuracy of 0.057 mm using only three 1-bit SPPWM patterns. Furthermore, different simulation noises were used to demonstrate the robustness of the proposed method.