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Abstract <p>Addressing issues such as high parameter count and low detection accuracy in weed detection algorithms, this paper presents a study on a weed detection algorithm based on an improved YOLOv5s model. Firstly, the backbone network was enhanced using the ParC modul4e to reduce computational demands and increase model detection speed; secondly, the C3BRA module, designed based on the BRA attention mechanism, replaced the original C3 module to focus on the extraction and reinforcement of key feature information; finally, the SIoU loss function replaced the CIoU loss function, accelerating network convergence and improving model detection accuracy. Experimental validation on the test dataset compared the improved model with the original YOLOv5s model, showing that the modified model increased the <i>P</i> value by 2.8%, the mAP value by 1.7%, and reduced model parameters by 10.7%, better meeting the requirements for weed detection.</p>

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A Weed Detection Algorithm Based on Improved YOLOv5S

  • 期刊论文

  • 发表日期：

    2025年8月1日

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  • Yuehua Li,
  • Zhangyan Yao,
  • Yuyang Gu,
  • Bin Hu

摘要

Abstract

Addressing issues such as high parameter count and low detection accuracy in weed detection algorithms, this paper presents a study on a weed detection algorithm based on an improved YOLOv5s model. Firstly, the backbone network was enhanced using the ParC modul4e to reduce computational demands and increase model detection speed; secondly, the C3BRA module, designed based on the BRA attention mechanism, replaced the original C3 module to focus on the extraction and reinforcement of key feature information; finally, the SIoU loss function replaced the CIoU loss function, accelerating network convergence and improving model detection accuracy. Experimental validation on the test dataset compared the improved model with the original YOLOv5s model, showing that the modified model increased the P value by 2.8%, the mAP value by 1.7%, and reduced model parameters by 10.7%, better meeting the requirements for weed detection.