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基于YOLOv3的改进仪表检测算法 被引量:1

An Improved Instrument Detection Algorithm Based on YOLOv3
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摘要 仪表检测是智能仪表测试不可或缺的环节,其效果直接决定仪表测试的准确率。针对仪表检测背景复杂且要求速度快的特点,提出一种基于改进YOLOv3的目标检测算法。基于YOLOv3算法,首先使用DenseNet(Densely Connected Convolutional Networks)替换Darknet中的最后2个网络块,以加强模型对特征的重用。然后采用轻量化的Darknet-46作为特征提取网络,并将DenseNet中的卷积神经网络修改为深度可分离卷积网络,再将所有检测层(YOLO Detection)之前的6层卷积修改为2层,以减少模型的参数。同时引入GDIOU(generalized-IOU and distance-IOU,GDIOU)边界框以回归坐标损失,并根据检测需求重新调整损失函数的权重。实验结果表明,相比原算法,改进的YOLOv3算法参数数量减少40%,在仪表检测中的精确率和召回率分别达到95.83%和94.98%,分别提高2.21个百分点和2.09个百分点,平均精度提高2.42个百分点,检测速度提高30.18%。 Instrument detection is an indispensable part of intelligent instrument testing,its effect directly determines the accuracy of instrument testing.In view of the complex positioning background of the instrument and the requirement of fast detection speed,a target detection algorithm based on improved YOLOv3 is proposed.Based on YOLOv3 algorithm,the last two network blocks in the Darknet are first replaced with DenseNet(Densely Connected Convolutional Networks)so as to enhance the reuse of features by the model.And then the lightweight Darknet-48 is used as feature extraction networks,and the convolution neural network in the DenseNet is modified to the deep separable convolution network,and then the 6 layer convolution before all detection layers(YOLO Detection)is modified to 2 layers so as to reduce the parameters of the model.At the same time,GDIOU bounding box is introduced to regress coordinates loss,and the weight of the loss function is readjusted according to the detection requirements.Experimental results show that compared with the original algorithm,the number of parameters of the improved YOLOv3 algorithm is reduced by 40%,and the accuracy and recall in instrument detection reach 95.83%and 94.98%,respectively,which is increased by 2.21 percentage points and 2.09 percentage points.The average accuracy is increased by 2.42 percentage points and the detection speed is increased by 30.18%.
作者 黄子平 黄继风 周小平 HUANG Zi-ping;HUANG Ji-feng;ZHOU Xiao-ping(College of Information,Mechanical and Electrical Engineering,Shanghai Normal University,Shanghai 201499,China)
出处 《计算机与现代化》 2022年第1期77-84,90,共9页 Computer and Modernization
基金 上海市科委2019年"科技创新行动"计划地方院校能力建设拟立项项目(19070502900)。
关键词 仪表检测 轻量化 密集层网络 深度可分离卷积 损失函数 instrument detection lightweight DenseNet depth separable convolution loss function
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