Scene object detection method based on improved YOLOv5

Gong Cheng

doi:10.1117/12.3025595

8 April 2024 Scene object detection method based on improved YOLOv5

Gong Cheng

Proceedings Volume 13090, International Conference on Computer Application and Information Security (ICCAIS 2023); 1309010 (2024) https://doi.org/10.1117/12.3025595
Event: International Conference on Computer Application and Information Security (ICCAIS 2023), 2023, Wuhan, China

Abstract

In order to solve the shortcomings of slow, time-consuming and high labor cost of manual object recognition and archiving tasks, a scene image object recognition method based on YOLOv5 is designed. Based on the YOLOv5 model, a virtual adversarial training mechanism is introduced to enhance the processed data to obtain new extended data to solve the problem of a small number of labeled samples in the semi-supervised learning classification model, and improve the robustness and generalization of the network structure. transformation ability. The experimental results show that the proposed YOLOv5s + VAT detection framework can effectively improve the detection accuracy of the network; the network accuracy can reach 97.042% in the experimental environment under our setting. The detection method can meet practical application requirements.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Gong Cheng "Scene object detection method based on improved YOLOv5", Proc. SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), 1309010 (8 April 2024); https://doi.org/10.1117/12.3025595

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