首页|融合BiFPN的轻量化YOLO v7疲劳检测方法

融合BiFPN的轻量化YOLO v7疲劳检测方法

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为了解决矿井提升机司机疲劳检测准确率低和实时性差等问题,提出一种融合BiFPN的轻量化YOLO v7 疲劳检测方法.该模型将YOLO v7 主干网络中会产生冗余的卷积计算用轻量级的Ghost网络代替来提取特征并将Ghost网络中指数型的激活函数换成轻量级激活函数SMU(Smooth Maximum Unit).融合双向特征金字塔(BiPFN)的轻量化YOLO v7 疲劳检测模型在自建矿井提升机司机疲劳驾驶数据集进行实验,结果表明:平均精度达到了97.25%,实时性达到了78 FPS,相较于原始的YOLO v7 网络精度提升了3.14%,速度提高了8 FPS.
Lightweight YOLO v7 Fatigue Detection Method Based on BiFPN Fusion
To address the issue of low fatigue detection accuracy and poor real-time performance of hoist drivers,this paper proposes a lightweight fatigue detection method based on BiFPN fusion.This model chooses YOLO v7,which has a faster single-stage detection speed,as the target detector the redundant convolution calculations in the YOLO v7 backbone network are replaced with a lightweight Ghost network to extract features,and the exponential activation function in Ghost network is replaced by lightweight SMU(Smooth Maximum Unit)activation function.The lightweight YOLO v7 fatigue detection model with Bidirectional feature pyramid(BiPFN)is tested on the self-built dataset of mine hoist driver fatigue driving.The results show that the average accuracy reached 97.25%,the real-time performance reached 78 FPS(frames per second),which increased the accuracy by 3.14%and the speed by 8 FPS compared with the original YOLO v7 network.

Fatigue detectionBiFPNYOLO v7Ghost

秦心茹、吴涛

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安徽理工大学 计算机科学与工程学院,安徽 淮南 232000

疲劳检测 BiFPN YOLO v7 Ghost

安徽理工大学研究生创新基金

2022CX2124

2024

兰州工业学院学报
兰州工业学院

兰州工业学院学报

影响因子:0.205
ISSN:1009-2269
年,卷(期):2024.31(2)
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