Research on Tool Detection Algorithm based on YOLOv8 Improved Model

Wanpeng Qi

doi:10.54097/as9vey12

Authors

Wanpeng Qi

DOI:

https://doi.org/10.54097/as9vey12

Keywords:

YOLOv8; SE Attention Mechanism; Loss Function SIOU; mAP.

Abstract

With the continuous improvement of people's living standards, the public's awareness of protecting the right to life safety has become increasingly important, and higher requirements have been put forward for the prevention of public safety. Controlled cutting tools are a part of cutting tools, which are controlled and managed by law to prevent criminals from using them as weapons for illegal activities. To prevent criminals from committing crimes in places with high population mobility, such as subways, campuses, airports, and other enclosed areas with high population mobility. This article proposes a controlled tool detection algorithm based on the improved YOLOv8 model to improve the detection accuracy and efficiency of controlled tools. After experiments, SE attention mechanism was added to the original YOLOv8 algorithm model, and the loss function SIOU was added to the YOLOv8 algorithm. The improved mAP increased from the initial 80.8% to the improved 85%, an increase of 4.2%. This provides a certain reference value for future algorithm models for identifying controlled tools.

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