一种基于视觉的库区可疑目标识别算法研究

Research on a Vision-Based Suspicious Target Recognition Algorithm in Reservoir Area

针对库区巡检图像采集设备对图像目标智能识别需求,进行基于视觉的远距离可疑目标识别算法设计与实现.采用目标检测算法对图像进行目标识别并采集,通过基于卷积神经网络的深度学习模型卷积层对目标图像提取特征,采用基于机器学习传统方法的浅层网络对特征进行可疑目标分类.根据算法设计实验,实验结果表明本算法模型识别效果良好,可有效减少人工识别工作量,能满足实际应用需要要求.

Aiming at the requirement of intelligent recognition of image targets by the storage area inspection image acquisition equipment,a vision-based long-distance suspicious target recognition algorithm was designed and implemented.Firstly,a target detection method was used to identify and collect the target image.And then,the convolution layer of the deep learning model based on convolutional neural network was used to extract the features of the target image,and the shallow network based on the traditional machine learning method was used to classify the suspicious target.Finally,an experiment was designed according to the algorithm.The experimental results show that the algorithm model can improve recognition effect,can effectively reduce the workload of manual recognition,and can meet the requirements of actual application.