A neural network pavement crack identification method combined with discreteness analysis is proposed. After grey transformation, image enhancement, the images are divided to two groups, one for training, the other one for test. The images in training group are divided into a series of sub blocks. The sub blocks contain cracks are taken as positive samples, and the sub blocks with shadows and normal roads are taken as negative samples. The two samples are used for extracting features, and the features are used to training model, and the model is used to recognize the crack in test group. For little error recognition points, a discreteness analysis was proposed to solve this problem. The contrast recognition of clean and shadowed pavement in gray value method and our method was carried out on asphalt and cement pavement respectively. Experimental result shows that the traditional gray value method is of little difference to neural network method combined with discreteness analysis in clean road, while big difference in shadow road.
For the Tiangong-1/Shenzhou-spaceship rendezvous and docking mission, the problems in the work of rendezvous and docking sensors in various complicated environments were analyzed. To improve the measurement accuracy of sensors, measurement and research work on the BRDF (Bidirectional Reflectance Distribution Function) of spacecraft cladding materials was conducted in terms of stray light analysis in optical systems. In order to verify the applicability of the BRDF data, the rendezvous and docking sensor was chosen as the analysis object, to compare the changes of the imaging quality of the optical system before and after using the BRDF data.
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