Abstract
The presented article focuses on the application of deep neural networks for recognizing selected types of objects in digital images. Various learning techniques, network architectures, and hyperparameters were analyzed to optimize the detection quality. This study compared supervised learning, self-supervised learning, and transfer learning methods, with YOLOv8 showing the highest effectiveness in the transfer learning approach. This article is of a typical application nature. The results included in it can be used by a potential reader for other applications related to object recognition in digital images.
| Original language | English |
|---|---|
| Article number | 7931 |
| Journal | Applied Sciences (Switzerland) |
| Volume | 15 |
| Issue number | 14 |
| DOIs | |
| Publication status | Published - Jul 2025 |
Keywords
- artificial intelligence
- deep neural networks
- image classification
- machine learning
- object recognition
ASJC Scopus subject areas
- General Materials Science
- Instrumentation
- General Engineering
- Process Chemistry and Technology
- Computer Science Applications
- Fluid Flow and Transfer Processes
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