The core task of target recognition is to distinguish the type of target of an image.Target recognition is usually completed using machine learning algorithms,and robust feature extraction is its key.However,traditional manually extracted features often have weak expressive power.The rise of deep learning theory provides an effective solution for high-precision target recognition,and due to its rich theory and wide coverage,it also brings new challenges to the teaching of target recognition courses.Through designing the professional course content,constructing the knowledge chain,collaborative construction of active classrooms between teachers and students,and exploring comprehensive assessment models,teaching reform is carried out.In terms of explaining the content of deep neural network for target recognition,the focus is on introducing key concepts,classical networks,research progress,and future development directions,which may provide reference for teachers and students engaged in related work.
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