Hierarchical detection of unsafe conditions in banks based on YOLOv8

Shuping Jiang; Yuansong Li

doi:10.1117/12.3038014

22 August 2024 Hierarchical detection of unsafe conditions in banks based on YOLOv8

Shuping Jiang, Yuansong Li

Proceedings Volume 13228, Fifth International Conference on Computer Communication and Network Security (CCNS 2024); 132280T (2024) https://doi.org/10.1117/12.3038014
Event: Fifth International Conference on Computer Communication and Network Security (CCNS 2024), 2024, Guangzhou, China

Abstract

This study proposes an improved YOLOv8 algorithm based on the network model framework in response to issues in grading accuracy, slow speed, high false alarm rate, and the excessive workload of monitoring staff encountered in the tiered monitoring of unsafe banking conditions. These challenges lead to less effective monitoring results. The study utilized a dataset that includes PASCAL VOC2007 and a section of a self-built dataset, collectively comprising of 8,511 images. Initially, a Partial Convolutions module was introduced to the backbone section, enhancing the ability to recognize occluded objects in images and improving the resistance to interference. Following that, an enhancement was made to the Bottleneck structure, integrating Large Kernel Attention (LSKA) and enhancing its detection capabilities for small targets. For large target detection, Multi-Scale Dilated attention was employed to enhance the model's processing efficiency and detection accuracy. Lastly, an ASFF head was incorporated. Overall, the improvements resulted in a 3.0% increase in mAP@0.5, boosting detection accuracy and speeding up the detection process.

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

Citation Download Citation

Shuping Jiang and Yuansong Li "Hierarchical detection of unsafe conditions in banks based on YOLOv8", Proc. SPIE 13228, Fifth International Conference on Computer Communication and Network Security (CCNS 2024), 132280T (22 August 2024); https://doi.org/10.1117/12.3038014

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KEYWORDS

Object detection

Fire

Data modeling

Small targets

Performance modeling

Video surveillance

Target detection

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