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The traditional calibration paradigms fail to give reliable and accurate results in case of low-quality 2D planar calibration plates. In this paper, an active method is proposed by employing an LCD panel for camera calibration. This method automatically generates a sequence of virtual patterns in different views by pre-defined transforms without manually manipulation or other equipment’s help to move the patterns. Then, the projections of virtual patterns are captured by a camera. The homography between projective patterns in virtual world coordinate and their images is calculated directly to obtain the camera parameters. Experimental results show that the calibration error is 0.018 pixel in terms of mean re-projection error by using 18 virtual patterns, which is significantly less than the state-of-the-art methods. The proposed scheme makes camera calibration flexible and easy to use.

The internal leakage of a hydraulic cylinder is an inevitable hydraulic system failure that seriously affects the working efficiency of the hydraulic system. Therefore, it is very important to accurately identify and predict leakage data in the hydraulic cylinder. In this paper, a model is proposed to simulate a small internal leakage of hydraulic cylinders, to convert the amount of leakage of hydraulic oil into strain signals through high-precision strain gauges and to train the collected strain signals using various neural networks to form a computational model and obtain prediction results from the model. The neural networks applied in this paper are convolutional neural networks, BP neural networks, T-S neural networks and Elman neural networks. The predicted results of the neural network are compared with the actual leakage amount. The results show that the prediction accuracy of the above four kinds of neural networks are all above 90%, of which the convolutional neural network is the most accurate. This research provides scientific and technical support for measuring and predicting small leaks.

It is challenging to align multi-exposed images due to large illumination variations, especially in presence of saturated regions. In this paper, a novel image alignment algorithm is proposed to cope with the multi-exposed images with saturated regions. Specifically, the multi-exposed images are first normalized by using intensity mapping functions (IMFs) in consideration of saturated pixels. Then, the normalized images are coded by using the local binary pattern (LBP). Finally, the coded images are aligned by formulating an optimization problem by using a differentiable Hamming distance. Experimental results show that the proposed algorithm outperforms state-of-the-art alignment methods for multi-exposed images in terms of alignment accuracy and robustness to exposure values.

The intelligent estimation of degradation state and the prediction of remaining useful life (RUL) are important for the maintenance of industrial equipment. In this study, the degradation process of equipment is modeled as an improved hidden semi-Markov model (HSMM), in which the dependence of durations of adjacent degradation states is described and modeled in the HSMM. To avoid underflow problem in computing the forward and backward variables, a modified forward–backward algorithm is proposed in the HSMM. Based on the improved algorithm, online estimation of degradation state and the distribution of RUL can be obtained. Case studies on tool wearing diagnosis and prognosis have verified the effectiveness of this model.

The traditional estimation methods of space targets pose are based on artificial features to match the transformation relationship between the image and the object model. With the explosion of deep learning technology, the approach based on deep neural networks (DNN) has significantly improved the performance of pose estimation. However, the current methods still have problems such as complex calculation, low accuracy, and poor real-time performance. Therefore, a new pose estimation algorithm is proposed in this paper. Firstly, the mask image of the target is obtained by the instance segmentation algorithm, and its point cloud image is obtained based on a depth map combined with camera parameters. Finally, the correlation among points is established to realize the prediction of pose based on multi-modal feature fusion. Experimental results in the YCB-Video dataset show that the proposed algorithm can recognize complex images at a speed of about 24 images per second with an accuracy of more than 80%. In conclusion, the proposed algorithm can realize fast pose estimation for complex stacked objects and has strong stability for different objects.

Object detection and segmentation can improve the accuracy of image recognition, but traditional methods can only extract the shallow information of the target, so the performance of algorithms is subject to many limitations. With the development of neural network technology, semantic segmentation algorithms based on deep learning can obtain the category information of each pixel. However, the algorithm cannot effectively distinguish each object of the same category, so YOLOMask, an instance segmentation algorithm based on complementary fusion network, is proposed in this paper. Experimental results on public data sets COCO2017 show that the proposed fusion network can accurately obtain the category and location information of each instance and has good real-time performance.

It is challenging to convert a hazy color image into a gray-scale image because the color contrast field of a hazy image is distorted. In this paper, a novel decolorization algorithm is proposed to transfer a hazy image into a distortion-recovered gray-scale image. To recover the color contrast field, the relationship between the restored color contrast and its distorted input is presented in CIELab color space. Based on this restoration, a nonlinear optimization problem is formulated to construct the resultant gray-scale image. A new differentiable approximation solution is introduced to solve this problem with an extension of the Huber loss function. Experimental results show that the proposed algorithm effectively preserves the global luminance consistency while represents the original color contrast in gray-scales, which is very close to the corresponding ground truth gray-scale one.

A robot can identify the position of a target and complete a grasping based on the hand–eye calibration algorithm, through which the relationship between the robot coordinate system and the camera coordinate system can be established. The accuracy of the hand–eye calibration algorithm affects the real-time performance of the visual servo system and the robot manipulation. The traditional calibration technique is based on a perfect mathematical model AX = XB, in which the X represents the relationship of (A) the camera coordinate system and (B) the robot coordinate system. The traditional solution to the transformation matrix has a certain extent of limitation and instability. To solve this problem, an optimized neural-network-based hand–eye calibration method was developed to establish a non-linear relationship between robotic coordinates and pixel coordinates that can compensate for the nonlinear distortion of the camera lens. The learning process of the hand–eye calibration model can be interpreted as B=fA, which is the coordinate transformation relationship trained by the neural network. An accurate hand–eye calibration model can finally be obtained by continuously optimizing the network structure and parameters via training. Finally, the accuracy and stability of the method were verified by experiments on a robot grasping system.

According to the hydraulic principle diagram of the subgrade test device, the dynamic pressure cylinder electrohydraulic servo pressure system math model and AMESim simulation model are established. The system is divided into two parts of the dynamic pressure cylinder displacement subsystem and the dynamic pressure cylinder output pressure subsystem. On this basis, a RBF neural network backstepping sliding mode adaptive control algorithm is designed: using the double sliding mode structure, the two RBF neural networks are used to approximate the uncertainties in the two subsystems, provide design methods of RBF sliding mode adaptive controller of the dynamic pressure cylinder displacement subsystem and RBF backstepping sliding mode adaptive controller of the dynamic pressure cylinder output pressure subsystem, and give the two RBF neural network weight vector adaptive laws, and the stability of the algorithm is proved. Finally, the algorithm is applied to the dynamic pressure cylinder electrohydraulic servo pressure system AMESim model; simulation results show that this algorithm can not only effectively estimate the system uncertainties, but also achieve accurate tracking of the target variables and have a simpler structure, better control performance, and better robust performance than the backstepping sliding mode adaptive control (BSAC).

In this paper, we propose a novelradiometric response function estimationalgorithm for high dynamic range imaging. In this algorithm, imagesarecaptured in random time interval of asame scene. The camera response function (CRF)is estimated based on each image and obtained the average CRF. Then, the ratio of eachCRF and the average value is definedas acontrol factor tofinally determine the optimal imageCRF. Wepresent an image fusion method to fuse all the images to generate an optimal high dynamic range image. Results on a variety of benchmarked and real images demonstrate that the proposed approach improves image quality and can obtain the best high dynamic range (HDR) image.

In this paper, we propose a novel tone mapping method to obtain high visibility by boosting local luminance contrast with guided image filter. Specifically, the original images are decomposed into multi-scale layers by the weighted least square filter. A novel pixel-wise weighting map employing the guided image filter on each layer is presented. The key idea is to assign large weightings to the regions with large contrast such that lighting effects are highly reduced and halo artifacts merely occur. Moreover, color balancing correction is performed to suppress color distortion. Experiments on several testing sets demonstrate the proposed method preserves clearer details and more faithful color tone than the existing methods in terms of visual and objective evaluation.

Subgrade bears both the impacts of the high-speed train and the weight of superstructures. Its stability affects the line smoothness directly, but it is very hard to simulate its working condition, and the in situ testing method is inadequate. The article presents a railway roadbed in situ testing device, and it proposes an excitation hydraulic cylinder system to output static and dynamic pressure simultaneously in order to simulate the force of the train. The dynamic following performance is quite poor without control algorithm, so the robust control block is adopted to improve the following performance of the excitation system under different frequencies and waveforms. In addition, considering the effects of the external disturbances, simulation is carried out with a certain intensity of noise to test the effectiveness of the control scheme. The simulation results show that the robust control algorithm makes the excitation system achieving much better following performance.

In this paper, a second-order consensus algorithm with input constraints was introduced. The condition of the algorithm for reaching consensus was given. Via Lyapnov stability criterion, the stability of the algorithm was proved. Based on this algorithm, a new control algorithm for swarm robots formation control was also proposed. Finally, from the simulation results of the above algorithms, it can be found that the algorithms with input constraints showed a better consensus performance than the algorithm without input constraints.