Smart Application of Deep Learning Technology in Bacterial Security Identification

Yan Zhao; Yaqin Zhang; Tuanjie Chu

doi:10.54691/f9q2yf56

Authors

Yan Zhao
Yaqin Zhang
Tuanjie Chu

DOI:

https://doi.org/10.54691/f9q2yf56

Keywords:

Bacterial Recognition; Artificial Intelligence; Deep Learning; Convolutional Neural Network.

Abstract

The rapid development of artificial intelligence technology has promoted the intelligence and automation of smart security systems, making security monitoring more efficient and accurate. Based on computer vision and natural language processing technology, smart security covers key technical fields such as face recognition, behavior monitoring and intelligent inspection. Although artificial intelligence technology has brought revolutionary changes to smart security, it still faces challenges such as privacy security, misidentification and missed identification, which need to be continuously improved and resolved. This paper deeply explores the application and development of artificial intelligence technology in smart security, and provides an important reference for future research and practice in the field of smart security. Bacteria are widely present in nature, and accurate identification of bacterial types is crucial in clinical diagnosis, food safety monitoring and other fields. However, traditional bacterial identification methods based on microscopy observation have many defects, are time-consuming and labor-intensive, and rely on manual experience. This paper explores methods for intelligent bacterial identification based on computer vision and deep learning technology. First, the application principles of deep learning in the field of image processing are introduced, especially the advantages of convolutional neural networks in bacterial image feature extraction. Then, the latest research progress of bacterial identification technology based on deep learning is reviewed, and the performance of various convolutional neural network models in bacterial classification tasks is commented on. Finally, the main challenges currently faced in this field are pointed out, providing a theoretical basis for promoting bacterial identification technology based on deep learning.

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