计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 424-428.doi: 10.11896/jsjkx.220100252
陈永平1, 朱建清1,2, 谢懿1, 吴含笑3, 曾焕强1
CHEN Yong-ping1, ZHU Jian-qing1,2, XIE Yi1, WU Han-xiao3, ZENG Huan-qiang1
摘要: 针对安全帽检测算法的快速且精准需求,提出了一种实时安全帽检测算法。首先,针对基于边界框回归损失函数容易出现梯度消失(Gradient Vanish)的问题,本文提出外接圆半径差(Circumcircle Radius Difference,CRD)损失函数;然后,针对复杂多尺度特征融合层制约检测速度的问题,提出了一种轻量化的小目标聚焦型(Focus on Small Object,FSO)特征融合层;最后本文结合YOLO网络、CRD和FSO形成YOLO-CRD-FSO(YCF)检测模型,实现实时安全帽检测。实验结果表明,在Jetson Xavier NX设备上检测分辨率为640×640的视频,YCF的检测速度达到43.4帧/秒,比当前最新锐的YOLO-V5模型的速度快了近2帧/秒,且均值平均精度提升了近1%。说明YCF检测模型综合优化了边界框回归损失函数和特征融合层,获得了良好的安全帽检测效果。
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