To realize automatic crack detection for aircraft skin, skin image processing and parameter estimation methods were studied based on scanning images obtained by pan-and-tilt long-focus camera. Firstly, considering the characteristics of aircraft skin images, light compensation, adaptive grayscale stretching, and local OTSU segmentation were carried out to obtain the binary images of cracks. Then, the characteristics like area and rectangularity of the connected domains were calculated to remove block noises in the images. After that, thinning and deburring were operated on cracks presented in the denoised binary images, and all branches of crack were separated by deleting the nodes of cracks. Finally, using the branch pixels as indexes, information of each crack branch such as the length, average width, maximum width, starting point, end point, midpoint, orientation, and number of branches were calculated by tracing pixels and the report was output by the crack detection software. The experimental results demonstrate that cracks wider than 1 mm can be detected effectively by the proposed method, which provides a feasible means for automatic detection of aircraft skin cracks in fuselage and wings.

Aiming to solve the change of the extrinsic parameters between the two cameras in free binocular stereo vision system caused by the rotation of the cameras, a method for acquiring the dynamic extrinsic parameters based on calibration of rotation axis was proposed. Multiple rotation and translation matrixes were obtained by the calibration at different positions, then the parameters of rotation axis could be calculated by using least square method. Combined with the intrinsic and extrinsic parameters at initial position and rotation angle, the dynamic extrinsic parameters between the two cameras could be calculated in real time. The chessboard corners were reconstructed with the dynamic extrinsic parameters calculated by the proposed method, the result showed that the average error was 0.241mm and the standard deviation was 0.156mm. Compared with the calibration method based on multiple-plane calibration target, the proposed method is easier to implement and has higher precision, where dynamic extrinsic parameters can be acquired without real-time calibration.

When civil aviation passengers check in, various uncertainty problems exist in the baggage tag readers' number, position and angle. To solve the problems, the Dynamic Population-Double Fitness Particle Swarm Optimization (DPDF-PSO) algorithm was proposed. Firstly, the mathematical model of baggage tag detector was established, then it was transformed into an optimization problem; secondly, the optimization problem was solved by standard Particle Swarm Optimization (PSO) algorithm; finally, the standard PSO algorithm was improved in accordance with the model features. The simulation results show that compared with standard PSO algorithm, the simulation time of the DPDF-PSO algorithm reduced by 23.6%, the objective function value increased by 3.7%. DPDF-PSO algorithm overcomes the shortage of long simulation time and troublesome problem of optimal boundary solutions existed in standard PSO algorithm. Identity information can be read quickly and accurately by readers layout at a lower cost.