While the global production of wind energy is increasing, there exists a significant gap in the academic and practice literature regarding the analysis of wind turbine accidents. Our paper presents the results obtained from the analysis of 240 wind turbine accidents from around the world. The main focus of our paper is revealing the associations between several factors and deaths and injuries in wind turbine accidents. Specifically, the associations of death and injuries with the stage of the wind turbine's life cycle (transportation, construction, operation, and maintenance) and the main cause factor categories (human, system/equipment, and nature) were studied. To this end, we conducted a detailed investigation that integrates exploratory and statistical data analysis and data mining methods. The paper presents a multitude of insights regarding the accidents and discusses implications for wind turbine manufacturers, engineering and insurance companies, and government organizations.

Stereo rig with wide baseline is necessary when accurate depth estimation for distant object is desired. However, in order to make calibration pattern to be viewed from both left and right cameras, the wider the baseline the bigger the calibration pattern is required. In contrast to the traditional stereo calibration method using calibration pattern, we propose a self-calibration approach that can estimate cameras' rotation matrices for stereo rig with wide baseline (3 m). Given images taken from left and right cameras, the relative roll and pitch angles between two cameras are recovered by aligning sea horizon in left and right images. The pitch angle is estimated by making the projections of one point at infinite distance appear at the same location in both images. A photometric minimization is applied to refine the rotation parameters. Compared with conventional checkerboard-based calibration techniques which require extra equipments or personnel, our approach only needs a pair of sea images. Moreover, unlike most self-calibration approaches, feature detection and matching are not required which makes it possible to apply our approach on featureless images. As a result, it is flexible and easy to implement our approach on sea surface images. Real world experiments demonstrate the feasibility of our approach.

A real time vision based long range object detection and tracking algorithm for unmanned surface vehicles (USV) is proposed in this paper. HD image (2736 × 2192) is utilised in this work to obtain high accuracy for the object distance estimation. With handling such high resolution images for real time performance, we propose a coarse to fine approach, which firstly estimates the sea surface plane and locations of objects coarsely on lower resolution images corresponding to the HD images, then the detected coarse locations or regions of interest (ROI) are projected to the original HD image, finally stereo matching is preformed in the original image only on these extracted ROI, which renders more accurate 3D information for localizing the objects on the open sea. In the tracking, we propose to combine the target tracking based on 2D image with the constrained template matching to compute the depth, which demonstrates a more robust and accurate performance. Experimental results with our own dataset verify the high efficiency of our proposed method.

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