Calibration of an underwater binocular vision system based on the refraction model.

Underwater measurement based on stereo vision attaches great importance to camera calibration. However, it is challenging to perform accurate calibration due to the significant refraction presented at the interfaces of air and water. To solve this problem, a calibration method for an underwater binocular vision system based on the optimized refractive model is proposed. First, conventional calibration is performed to obtain basic initial camera parameters using checkerboard images collected in the air. Then, an evolutionary multi-objective function is established according to Snell's law, the refractive light propagation path, and checkerboard geometric relationship. Finally, precise camera parameters and involved refraction parameters are both obtained for underwater target positioning and size measurement by the non-dominated sorting genetic algorithm of the reference point. A group of experiments is performed, and the validity and effectiveness of the proposed calibration algorithm is demonstrated.

Trinocular vision and spatial prior based method for ground clearance measurement of transmission lines.

It is an essential task to inspect ground clearance of transmission lines in time. However, the weak texture of transmission lines and high complexity of the background make it difficult to balance efficiency and accuracy. To solve the problem, a trinocular vision and spatial prior based method is proposed, which is specifically designed for ground clearance measurement of transmission lines with unmanned aerial vehicles (UAVs). In this novel method, a perpendicular double-baseline trinocular vision module is applied to improve the accuracy of transmission line reconstruction. Then the spatial prior information of geometric models under different shooting attitudes is analyzed in detail, and it is adopted to determine the ground crossing points and compute ground clearance efficiently. Also, an interactive software is developed and tested in the simulation environment of UAV inspection. Experimental results verify the feasibility of the measurement method. Finally, we discuss in detail how to apply the method effectively in practice and give a set of recommended camera parameters.