The Research On Pavement Roughness Detecting And The Intelligent Vision For Hoisting Equipment Base On Videogrammetry

This paper, based on the application of videogrammetry in engineering survey, designs a videogrammetry system for pavement roughness detection and an intelligent vision system for hoisting equipment, both of which are verified the feasibility through simulation experiments.The main contents of this paper are listed as follows:1. This paper introduces several common calibrate methods of videogrammetry and discusses their advantages and disadvantages and applications. The calibration methods of videogrammetry system for pavement roughness detection and intelligent vision system for cage hoist are introduced. Sub-pixel orientation technology, particularly the principle of Sub-pixel orientation, is studied for artificial mark. Meanwhile, it analyses the results from the errors of lens distortion through experiments.2. It proposes the application of videogrammetry with specific methods in pavement roughness measurements. Studying principles, it proposes the system module and process. The principles and feasibility are verified through simulation experiments. The method with dio-mark mutual deviation eliminates the jounce of reference platform so that the current way of using acceleration sensors could be replaced by. This method, which not only conquer the limitation and disadvantages of using acceleration sensors but also reduce cost by less using of sensors, is proved to be an efficient solution and practical popularization for pavement roughness measurements .3. It designs intelligent vision system for automatic leading of hoisting equipments. Studying principles, it proposes the system module and process. The principles and feasibility are verified through simulation experiments. This method can determine hoist direction with artificial mark intelligently through videogrammetry technology, therefore searching and lifting targets to destination can be automatic.4. It studies the processing data both of the systems. It analyzes the results of choosing white noise observed data polynomial optimal filter.5. Finally, it gives conclusions and analyzes the further research direction briefly.