基于PSO-BP网络的UUV视觉识别方法研究

Wei Zhang, Detao Meng, Zhicheng Liang, Ying Li Guo, Jiajia Zhou, Yunfeng Han
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引用次数: 2

摘要

在无人潜航器(UUV)恢复过程中,视觉传感器需要从引导图像中提取并识别所有光源。首先,针对无精确深度图的水下图像恢复问题,提出了一种基于散射模型的分割线性映射方法。恢复结果表明,该方法能够有效地突出细微细节,提高图像质量。然后结合改进的Snake模型,采用Canny边缘检测算法提取光源边缘及其不变矩;最后,设计了一种基于粒子群优化算法(PSOBP)的反向传播网络用于UUV识别,该网络比传统BP网络具有更高的识别率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on recognition method of UUV vision based on PSO-BP network
During unmanned underwater vehicle (UUV) recovery process, the vision sensor needs to extract and recognize all light sources from guided images. Firstly, Aiming at restoring underwater image without exact depth map, a segmented-linear-mapping approach based on scattering model is put forward. Restoring results indicate that the approach is capable of highlighting subtle details and improving image quality effectively. Then the light source edge and its invariant moments are extracted by Canny edge detection algorithm combined with improved Snake model. At last, a kind of Back Propagation network based on particle swarm optimization algorithm (PSOBP) for UUV recognition is designed, which demonstrates higher recognition rate than traditional BP network.
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