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由于外界条件的影响,光电测量系统会产生一定畸变,导致测量精度偏低。针对当前光电测量系统畸变校正方法存在的不足,以提高光电测量系统的测试精度为目标,提出了基于神经网络的光电测量系统畸变校正和优化方法。首先通过实验获取光电测量系统畸变的数据,并构建畸变的数学模型,然后采用神经网络对光电测量系统的畸变进行校正和优化,最后采用VC++编程程序实现畸变校正实验。结果表明,该方法可以消除外界条件的影响,提高了光电测量系统畸变校正的精度,比对方法校正精度提高了3%左右,可以应用于实际的光电测量系统中。
Due to the influence of external conditions, the photoelectric measurement system will produce some distortion, resulting in low measurement accuracy. Aiming at the shortcomings of current photogrammetric distortion correction methods, aiming at improving the measurement accuracy of photometric measurement system, a method of distortion correction and optimization of photoelectric measurement system based on neural network is proposed. Firstly, the data of the distortion of the photoelectric measurement system are obtained by experiments and the mathematical model of the distortion is established. Then, the distortion of the photoelectric measurement system is corrected and optimized by using the neural network. Finally, the distortion correction experiment is implemented by the VC ++ programming program. The results show that this method can eliminate the influence of external conditions and improve the accuracy of the distortion correction of the photoelectric measurement system. The accuracy of the calibration method is improved by about 3%, which can be applied to the actual photoelectric measurement system.