Review of deep learning based target detection algorithm research

Abstract

With the rapid development of the times, big data has started to affect all aspects of our life, and more and more products in industries are becoming carriers of big data. For example, CNNs with more implicit layers are proposed to meet our needs, although they have a more complex network structure. Compared with traditional machine learning, it has more powerful feature learning and feature representation ability, which can better meet the needs in the era of big data. Since its introduction, deep learning has achieved remarkable results in several large-scale recognition tasks in the field of computer vision. This paper presents a review of target detection methods based on a survey of domestic and international literature on the subject. Firstly, the current state of research in the field of target detection is introduced. Next, the development and rise of deep learning and convolutional neural networks are briefly introduced, and the basic structure of convolutional neural networks, convolutional feature extraction, and pooling operations are outlined. The analysis and discussion of deep learning-based target detection algorithms are focused, and the current shortcomings are pointed out. Finally, the applications of deep learning-based target detection and the future development directions are summarized.