To address the limitations of traditional target detection and recognition methods in complex battlefield envi-ronments,a deep convolutional neural network(CNN)-based method for extracting image features and localizing tar-gets is proposed in this paper.Traditional shape and texture features as well as the hotspot information in infrared ima-ges are comprehensively considered,and the accuracy of target detection and identification is improved through the training of large-scale labeled datasets and the optimization of the back propagation algorithm.Compared with tradi-tional methods,the method can automatically learn the feature representations in images without relying on manually designed features and classifiers.In order to verify the effectiveness of the algorithm,this paper selects the Hathi Hi3559AV100 as the core processing chip to design the hardware platform,and by porting the algorithm to this plat-form,the collected data samples are analyzed and tested,and the experimental results show that the system exhibits relatively stable performance in complex background environments,and is able to reliably perform target detection and recognition.
关键词
深度学习/目标检测/卷积神经网络/红外图像
Key words
deep learning/target detection/convolutional neural networks/infrared images
维普
万方数据