Multi-Object Image Classification Using Deep Learning Method
Corressponding author's email:
dinhnt@huit.edu.vnDOI:
https://doi.org/10.54644/jte.2024.1538Keywords:
Image Classification, Multi-object Image, Deep Learning, Object Recognition, YOLOv8Abstract
Image classification is an interesting topic for many scientists to improve the effectiveness of object recognition and image classification in computer vision. There are many techniques for image classification, in which deep learning methods have had many results in the problem of recognizing and classifying objects on an image. This paper performs a method for multi-object image classification using the YOLOv8 deep learning network. Firstly, each multi-object image is segmented into single-object images. Secondly, the identified image area, and then extracted feature vectors. Finally, the image is classified using the YOLOv8 deep learning network. An experiment conducted on the Flickr image set has shown better results than other methods and an average image classification result of 0.8872. Experimental results show that a proposed method using the YOLOv8 deep learning network for multi-object image sets is effective and can be applied to image data sets in many fields such as agriculture, traffic, and others.
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