深度学习在舰船前方障碍物图像识别中的应用

Application of deep learning in image recognition of obstacles ahead of ships

在舰船前方障碍物图像识别中,传统的图像识别系统漏检概率高、检测性能较差,难以满足舰船安全航行要求。深度学习是一种智能化技术,将其应用到障碍物图像识别中可以提高图像信息筛选、计算和检测效率。本文介绍深度学习中的神经网络模型和卷积神经网络,以及深度学习在舰船前方障碍物图像识别中的检测方法。基于传统深度学习算法的不足,提出改进后的Faster R-CNN方法在障碍物图像识别中的应用,并通过对比实验进行论证,实验结果表明改进后的图像识别模型具备检测精度高的应用优势。

In the image recognition of obstacles ahead of the ship,the traditional image recognition system has a high probability of missed detection and poor detection performance,which is difficult to meet the requirements of safe navigation of ships.Deep learning is an intelligent technology,and its application to obstacle image recognition can improve the efficiency of image information screening,calculation and detection.This paper introduces the neural network model and convolutional neural network in deep learning,and the detection method of deep learning in the image recognition of obstacles in front of ships.Based on the shortcomings of traditional deep learning algorithms,an improved Faster R-CNN method is proposed in the application of obstacle image recognition is demonstrated by comparison experiments.The experimental results show that the improved image recognition model has the application advantages of high detection accuracy.