YOLOv8-based small-target disinfection region detection model under complex indoor conditions

Yingming Feng; Anqi Qiu; Qiyuan Zhang; Kaikai Zhang; Yunwei Zou

doi:10.1117/12.3049992

11 December 2024 YOLOv8-based small-target disinfection region detection model under complex indoor conditions

Yingming Feng, Anqi Qiu, Qiyuan Zhang, Kaikai Zhang, Yunwei Zou

Author Affiliations +

Proceedings Volume 13441, International Conference on Cloud Computing and Communication Engineering (CCCE 2024); 134410C (2024) https://doi.org/10.1117/12.3049992
Event: International Conference on Cloud Computing and Communication Engineering (CCCE 2024), 2024, Nanjing, China

Abstract

In response to the challenge presented by the wide range of scenarios and targets in intricate disinfection environments, this study introduces an improved target detection algorithm. The algorithm integrates a Coordinate Attention mechanism, thereby enhancing the ability to capture information related to small targets. Additionally, the SPPFCSPC module effectively enhances the feature extraction capabilities of the backbone feature network. To address issues related to slow convergence and imprecise regression outcomes, the Focal and Efficient IOU loss function is utilized. Experimental findings demonstrate that the proposed algorithm achieves a remarkable mean Average Precision of 91.4%, representing a notable enhancement of 2.4% over the YOLOv8n model.

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

Citation Download Citation

Yingming Feng, Anqi Qiu, Qiyuan Zhang, Kaikai Zhang, and Yunwei Zou "YOLOv8-based small-target disinfection region detection model under complex indoor conditions", Proc. SPIE 13441, International Conference on Cloud Computing and Communication Engineering (CCCE 2024), 134410C (11 December 2024); https://doi.org/10.1117/12.3049992

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