Assessing the safety status of cranes is an important problem. To overcome the inaccuracies and misjudgments in such assessments, this work describes a safety assessment method for cranes that combines entropy and cumulative prospect theory. Firstly, the proposed method transforms the set of evaluation indices into an evaluation vector. Secondly, a decision matrix is then constructed from the evaluation vectors and evaluation standards, and an entropy-based technique is applied to calculate the index weights. Thirdly, positive and negative prospect value matrices are established from reference points based on the positive and negative ideal solutions. Thus, this enables the crane safety grade to be determined according to the ranked comprehensive prospect values. Finally, the safety status of four general overhead traveling crane samples is evaluated to verify the rationality and feasibility of the proposed method. The results demonstrate that the method described in this paper can precisely and reasonably reflect the safety status of a crane.

Transverse vibration measurement of beam structures based on computer vision has been widely used, but most visual measurement methods require the installation of artificial markers at the measured positions. This makes the measurement of vibrations in some key position of beam structures difficult due to their inaccessibility. Therefore, the development of target-less approaches is important for some engineering applications. In this paper, a method for measuring displacement and vibration of beam structures visually is presented based on sub-pixel edge point tracking. Combining the principle of coordinate space transformation and the two-dimensional normal distribution characteristics of false edge parameters, false edges are eliminated to determine the position of structural edges accurately. This allows measurement of beam deflection and point tracking of the beam structure’s vibration. The effectiveness of the proposed method is verified through a cantilever beam vibration experiment and the measured data are compared using a traditional piezoelectric sensor (eddy current displacement sensor and accelerometer) and digital image correlation method. Compared with the traditional piezoelectric sensor methods, the maximum difference of the measured displacement was less than 2.4%. Compared with the digital image correlation method, the time series correlation of the proposed method with the reference measurements was 0.35% higher, and the main vibration frequency error was 0. The experiment proves that the proposed method can be used as an effective visual measurement method for target-less tracking of the transverse vibration of beam structures.

Camera calibration is a crucial step in binocular measurement, and the accuracy of camera calibration largely determines the measurement accuracy of binocular vision. However, the calibration accuracy of existing calibration methods is difficult to satisfy the requirements of variable calibration environments and engineering applications. Therefore, based on the principle of Zhang's calibration method, a calibration method is proposed by combining bundle adjustment and diagonal constraints of the calibration target. Firstly, the improved Canny edge extraction algorithm is used to obtain the sub-pixel center of mass of ellipse (CME). Then, the Zhang's calibration method is used to obtain the initial values of the calibration parameters. The camera calibration parameters are optimized by using the bundle adjustment. Finally, diagonal constraints are used to further optimize the extrinsic camera position parameters. The experimental results show that compared to the other excellent methods, the proposed method can significantly improve calibration accuracy and has certain engineering application value.

Crane safety evaluation is significant for reducing the casualties and economic losses. Various evaluation methods have been applied to evaluate crane safety. However, when index standard with respect to every level is described in terms of intervals, existing crane safety evaluation models are not ideal. Although variable fuzzy sets (VFS) method has successfully solved similar problems, its evaluation processes are rather complex complicated and tedious. In this paper, we provided an improved VFS evaluation method with normal membership function for solving crane safety problem, in which the evaluation criteria are described in terms of intervals. To demonstrate our presented method, the improved variable fuzzy sets (IVFS) method was applied to two numerical cases including an application to crane safety assessment. It is shown that our proposed method possesses the merit to simplify assessment processes of traditional VFS and can be applied to assess crane safety when criteria values are defined as interval numbers.

During camera calibration, the plane target image is required to occupy most of the whole image, and the focused image is also needed. It is difficult to satisfy these conditions in large-scale structural vision measurement. Therefore, it is very important to explore a camera calibration method with a small calibration board for a large field of view. This paper proposed an out-of-focus image calibration method based on the accurate positioning of concentric circular projection center, which is not limited by the camera’s field of view and can overcome the influence of out-of-focus image on camera calibration. The proposed method consists of three parts: adaptive blurred circle image enhancement, accurate positioning of concentric circle projection center and camera calibration of concentric circle center point array. The simulation results show that under different Gaussian blur kernel radius conditions, the proposed accurate positioning method of the concentric circle projection center has better robustness. When Gaussian kernel radius K=40, the positioning accuracy of the proposed method is improved by 41.88%.In the real image experiment, the internal parameters of the camera in the heavy defocus image have similar results to the Zhang’s calibration method in the focused image. Both simulation and real image experiments show that the proposed method can realize the calibration of large targets with a small plane calibration board.

The 3D similarity coordinate transformation is widely used to estimate the transformation parameters for measurement datum transformation. Accurate and reliable transformation parameters are crucial for accurate and reliable data integration. However, the accuracy of the transformation parameters can be significantly affected or even severely distorted when the observed coordinates are contaminated by gross errors. To address this problem, an advanced iteratively weighted least squares solution based on the weighted least squares is proposed. This solution utilizes the singular value decomposition method to obtain the rotation matrix and introduces a novel weight estimation approach based on Gaussian function. This approach enables the weight to be normalized and optimized iteratively. To verify the accuracy and reliability of the proposed algorithm, the root mean square errors from both true and pseudo-observed values are analyzed by simulation experiments. Furthermore, the results of simulated and empirical experiments show that the proposed algorithm can effectively reduce the influence of gross errors to obtain reliable measurement datum transformation parameters. It should be noted that the new algorithm can easily be extended to the 2D/3D affine and rigid transformation cases, such as image matching, point cloud registration, and absolute orientation of photogrammetry.