False cracks, such as split joints and scratches, have macroscopic geometry that is similar to real cracks, which can influence the crack detection efficiency for a concrete bridge. To solve this problem, a crack detection algorithm based on the mesoscale geometric features of cracks is proposed. Through the mesoscale analysis of concrete crack formation and propagation mechanisms, it is found that a concrete crack propagates at the interface between aggregates and mortar and usually has a meandering path, whereas a false crack’s path is usually smooth or even straight. Thus the path smoothness of a crack candidate is chosen as the detection basis. The algorithm extracts a crack candidate with conventional methods, and also its skeleton for representing the path. In addition, the feature parameters are designed to quantify the path smoothness. Moreover, a back propagation neural network (BPNN) and a support vector machine (SVM) for the classification of crack candidates are trained using the proposed feature parameters as the input. Experimental results show that the classification rate of the BPNN trained by new features is 91.7%, which is better than the BPNNs trained by conventional features. The classification rate of the SVM is 93.3%, which is more suitable for engineering in small size samples.
This paper consults and improves the on hand computational methods and circuits, which comprehensively utilizes the
knowledge of the Aerodynamics, the heat transfer theory, the radio optics, ANSYS and so on. In the analysis of the IR
characteristics of aerial targets, taking it into account that most of the computing methods on hand are empirical or
semi-empirical, which are more simple, but more limited, less sufficient and scientific and have more human factors, so
we begin with the determination of the thermal field, adopt the numerical method to realize the calculation and modeling
of the IR radiation with ANSYS, analysis how the spectral coverage and the observed bearing affect the IR radiation, and
then get the credible and all-side numerical calculation results.
Then, this paper introduces a method utilizing 3DS MAX and OpenGL to generate the IR picture of the target,
which divides the grey level of the IR radiation reasonably according to the final numeric calculating results and
the principle of the grey level division, and then we generate the IR pictures of the aerial targets.