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Academic Journal of Computing & Information Science, 2023, 6(12); doi: 10.25236/AJCIS.2023.061211.

Research on location target detection algorithm based on neural network


Long He1, Zhou Yang2, Xiaoyu Feng2

Corresponding Author:
Long He

1School of Intelligent Technology and Engineering, Chongqing University of Science & Technology, Chongqing, China

2School of Intelligent Technology and Engineering, Chongqing University of Science & Technology, Chongqing, China


Object detection is a key problem in the field of computer vision, which has a wide range of applications, such as automatic driving, security monitoring, medical image analysis and so on. With the rapid development of deep learning technology, object detection algorithm based on neural network has become one of the research hotspots. In this paper, the object detection algorithm based on neural network is deeply studied, and its principle, method and application field are discussed. Firstly, this paper introduces the difference between traditional target detection algorithm and neural network-based target detection algorithm. Traditional algorithms usually rely on hand-designed feature extraction methods, while neural network-based algorithms automatically learn image features through convolutional neural networks (CNN), which has stronger generalization ability. In addition, we discuss in detail the basic concepts of deep learning techniques, including convolutional layers, pooling layers, fully connected layers, etc., and commonly used neural network structures. Secondly, this paper focuses on the key problems in object detection algorithms. We analyze the differences in the performance of various neural network architectures in solving these problems, and these methods have made significant progress in improving detection accuracy and reducing false detection rates. Further, this paper studies the data sets and evaluation indicators in the field of target detection. We cover some commonly used evaluation metrics such as accuracy, recall, F1 score, and mAP. These data sets and metrics are essential for the training and evaluation of algorithms, helping researchers compare the performance of different algorithms. Finally, this paper discusses the potential of target detection algorithm based on neural network in practical application. We highlight its wide application in several fields and discuss future research directions. With the improvement of hardware performance and the continuous development of deep learning technology, target detection algorithms based on neural networks will continue to make breakthroughs, bringing more innovation to various fields.


neural networks, CNN, YOLO, feature extraction

Cite This Paper

Long He, Zhou Yang, Xiaoyu Feng. Research on location target detection algorithm based on neural network. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 12: 97-105. https://doi.org/10.25236/AJCIS.2023.061211.


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