针对传统目测法检测贴片二极管表面缺陷效率低下和基于手工特征的目标检测算法模型较浅,以及语义性不高等问题,提出了改进YOLO-V4的贴片二极管表面缺陷检测方法。首先考虑到随着网络加深使梯度消失,以及减少网络中的特征冗余和参数量的...针对传统目测法检测贴片二极管表面缺陷效率低下和基于手工特征的目标检测算法模型较浅,以及语义性不高等问题,提出了改进YOLO-V4的贴片二极管表面缺陷检测方法。首先考虑到随着网络加深使梯度消失,以及减少网络中的特征冗余和参数量的情况,CSP1模块采用DenseNet替换原网络中的ResNet;其次,为了实现特征信息的跨维度交互,让网络更加关注重要信息,在CSP1模块后引入了三分支注意力机制模块,同时使用FPN+PANet对特征进行融合;并且用CSP2替换CBL×5模块,降低了网络的运算量,提高了算法检测速度;最后优化了Focal Loss函数,对正负样本添加权重,以解决正负样本不平衡的问题。本文算法相较于YOLO-V4的检测精度(precision,P)、召回率(recall,R)和多分类平均精度(mean average precision,mAP),分别高出2.98%,2.65%,2.92%,表明改进YOLO-V4可以有效检测贴片二极管表面缺陷问题。展开更多
为解决新型的双源无轨电车的集电杆自动识别集电盒并快速并网的问题,通过改进YOLO-V4(you only look once version 4)网络模型,得到SE-YOLO-POLY(squeeze and excitation networks-you only look once version 4-POLY)网络架构。采用该...为解决新型的双源无轨电车的集电杆自动识别集电盒并快速并网的问题,通过改进YOLO-V4(you only look once version 4)网络模型,得到SE-YOLO-POLY(squeeze and excitation networks-you only look once version 4-POLY)网络架构。采用该网络架构,解决了由于集电盒的大小不一致、高度不一致、拍照角度不一致导致识别的集电盒出现异动的形变和尺寸变化、无法顺利并网的问题。通过SE-YOLO-POLY网络的数据集的生成、模型的设计、训练环境、实际运行反标定方式的搭建等步骤完成网络的部署。改进的模型无论在训练时间、模型大小、识别精度还是在处理速度等方面,都优于传统网络,实现了复杂环境下新型的双源无轨电车的智能并网。展开更多
Traditional maize ear harvesters mainly rely on manual identification of fallen maize ears,which cannot realize real-time detection of ear falling.The improved You Only Look Once-V4(YOLO-V4)algorithm is combined with ...Traditional maize ear harvesters mainly rely on manual identification of fallen maize ears,which cannot realize real-time detection of ear falling.The improved You Only Look Once-V4(YOLO-V4)algorithm is combined with the channel pruning algorithm to detect the dropped ears of maize harvesters.K-means clustering algorithm is used to obtain a prior box matching the size of the dropped ears,which improves the Intersection Over Union(IOU).Compare the effect of different activation functions on the accuracy of the YOLO-V4 model,and use the Mish activation function as the activation function of this model.Improve the calculation of the regression positioning loss function,and use the CEIOU loss function to balance the accuracy of each category.Use improved Adam optimization function and multi-stage learning optimization technology to improve the accuracy of the YOLO-V4 model.The channel pruning algorithm is used to compress the model and distillation technology is used in the fine-tuning of the model.The final model size was only 10.77%before compression,and the test set mean Average Precision(mAP)was 93.14%.The detection speed was 112 fps,which can meet the need for real-time detection of maize harvester ears in the field.This study can provide technical reference for the detection of the ear loss rate of intelligent maize harvesters.展开更多
Suspicious fall events are particularly significant hazards for the safety of patients and elders.Recently,suspicious fall event detection has become a robust research case in real-time monitoring.This paper aims to d...Suspicious fall events are particularly significant hazards for the safety of patients and elders.Recently,suspicious fall event detection has become a robust research case in real-time monitoring.This paper aims to detect suspicious fall events during video monitoring of multiple people in different moving back-grounds in an indoor environment;it is further proposed to use a deep learning method known as Long Short Term Memory(LSTM)by introducing visual atten-tion-guided mechanism along with a bi-directional LSTM model.This method contributes essential information on the temporal and spatial locations of‘suspi-cious fall’events in learning the video frame in both forward and backward direc-tions.The effective“You only look once V4”(YOLO V4)–a real-time people detection system illustrates the detection of people in videos,followed by a track-ing module to get their trajectories.Convolutional Neural Network(CNN)fea-tures are extracted for each person tracked through bounding boxes.Subsequently,a visual attention-guided Bi-directional LSTM model is proposed for the final suspicious fall event detection.The proposed method is demonstrated using two different datasets to illustrate the efficiency.The proposed method is evaluated by comparing it with other state-of-the-art methods,showing that it achieves 96.9%accuracy,good performance,and robustness.Hence,it is accep-table to monitor and detect suspicious fall events.展开更多
To quickly detect and count the number of bayberry trees,this paper improves the YOLO-v4 model and proposes an optimal YOLO-v4 method for detecting bayberry trees based on UAV images.We used the Leaky_ReLU activation ...To quickly detect and count the number of bayberry trees,this paper improves the YOLO-v4 model and proposes an optimal YOLO-v4 method for detecting bayberry trees based on UAV images.We used the Leaky_ReLU activation function to accelerate the model extraction speed and used the DIoU NMS to retain the most accurate prediction boxes.In order to increase the recall rate of the object detection and construct the optimal YOLO-v4 model,the K-Means clustering method was embedded into DIoU NMS.We trained the model using UAV images of bayberry trees,it was determined that the optimal YOLO-v4 model threshold was 0.25,which had the best extraction effect.The optimal YOLO-v4 model had a detection accuracy of up to 97.78%and a recall rate of up to 98.16%on the dataset.The optimal YOLO-v4 model was compared with YOLO-v4,YOLO-v4 tiny,the YOLO-v3 model,and the Faster R-CNN model.With guaranteed accuracy,the recall rate was higher,up to 97.45%,and the detection of bayberry trees was better in different contexts.The result shows that the optimal YOLO-v4 model can accurately achieve the rapid detection and statistics of the number of bayberry trees in large-area orchards.展开更多
文摘针对传统目测法检测贴片二极管表面缺陷效率低下和基于手工特征的目标检测算法模型较浅,以及语义性不高等问题,提出了改进YOLO-V4的贴片二极管表面缺陷检测方法。首先考虑到随着网络加深使梯度消失,以及减少网络中的特征冗余和参数量的情况,CSP1模块采用DenseNet替换原网络中的ResNet;其次,为了实现特征信息的跨维度交互,让网络更加关注重要信息,在CSP1模块后引入了三分支注意力机制模块,同时使用FPN+PANet对特征进行融合;并且用CSP2替换CBL×5模块,降低了网络的运算量,提高了算法检测速度;最后优化了Focal Loss函数,对正负样本添加权重,以解决正负样本不平衡的问题。本文算法相较于YOLO-V4的检测精度(precision,P)、召回率(recall,R)和多分类平均精度(mean average precision,mAP),分别高出2.98%,2.65%,2.92%,表明改进YOLO-V4可以有效检测贴片二极管表面缺陷问题。
文摘为解决新型的双源无轨电车的集电杆自动识别集电盒并快速并网的问题,通过改进YOLO-V4(you only look once version 4)网络模型,得到SE-YOLO-POLY(squeeze and excitation networks-you only look once version 4-POLY)网络架构。采用该网络架构,解决了由于集电盒的大小不一致、高度不一致、拍照角度不一致导致识别的集电盒出现异动的形变和尺寸变化、无法顺利并网的问题。通过SE-YOLO-POLY网络的数据集的生成、模型的设计、训练环境、实际运行反标定方式的搭建等步骤完成网络的部署。改进的模型无论在训练时间、模型大小、识别精度还是在处理速度等方面,都优于传统网络,实现了复杂环境下新型的双源无轨电车的智能并网。
基金This work was funded and supported by the Shandong Provincial Key Science and Technology Innovation Engineering Project(Grant No.2018CXGC0217)the 13th Five-Year National Key Research and Development Program(Grant No.2018YFD0300606).
文摘Traditional maize ear harvesters mainly rely on manual identification of fallen maize ears,which cannot realize real-time detection of ear falling.The improved You Only Look Once-V4(YOLO-V4)algorithm is combined with the channel pruning algorithm to detect the dropped ears of maize harvesters.K-means clustering algorithm is used to obtain a prior box matching the size of the dropped ears,which improves the Intersection Over Union(IOU).Compare the effect of different activation functions on the accuracy of the YOLO-V4 model,and use the Mish activation function as the activation function of this model.Improve the calculation of the regression positioning loss function,and use the CEIOU loss function to balance the accuracy of each category.Use improved Adam optimization function and multi-stage learning optimization technology to improve the accuracy of the YOLO-V4 model.The channel pruning algorithm is used to compress the model and distillation technology is used in the fine-tuning of the model.The final model size was only 10.77%before compression,and the test set mean Average Precision(mAP)was 93.14%.The detection speed was 112 fps,which can meet the need for real-time detection of maize harvester ears in the field.This study can provide technical reference for the detection of the ear loss rate of intelligent maize harvesters.
文摘Suspicious fall events are particularly significant hazards for the safety of patients and elders.Recently,suspicious fall event detection has become a robust research case in real-time monitoring.This paper aims to detect suspicious fall events during video monitoring of multiple people in different moving back-grounds in an indoor environment;it is further proposed to use a deep learning method known as Long Short Term Memory(LSTM)by introducing visual atten-tion-guided mechanism along with a bi-directional LSTM model.This method contributes essential information on the temporal and spatial locations of‘suspi-cious fall’events in learning the video frame in both forward and backward direc-tions.The effective“You only look once V4”(YOLO V4)–a real-time people detection system illustrates the detection of people in videos,followed by a track-ing module to get their trajectories.Convolutional Neural Network(CNN)fea-tures are extracted for each person tracked through bounding boxes.Subsequently,a visual attention-guided Bi-directional LSTM model is proposed for the final suspicious fall event detection.The proposed method is demonstrated using two different datasets to illustrate the efficiency.The proposed method is evaluated by comparing it with other state-of-the-art methods,showing that it achieves 96.9%accuracy,good performance,and robustness.Hence,it is accep-table to monitor and detect suspicious fall events.
基金supported by the Fujian Science and Technology Program(grant numbers 2021Y0074)the Fujian Science and Technology Innovation Fund(grant numbers 2020SHQM14XIQM009).
文摘To quickly detect and count the number of bayberry trees,this paper improves the YOLO-v4 model and proposes an optimal YOLO-v4 method for detecting bayberry trees based on UAV images.We used the Leaky_ReLU activation function to accelerate the model extraction speed and used the DIoU NMS to retain the most accurate prediction boxes.In order to increase the recall rate of the object detection and construct the optimal YOLO-v4 model,the K-Means clustering method was embedded into DIoU NMS.We trained the model using UAV images of bayberry trees,it was determined that the optimal YOLO-v4 model threshold was 0.25,which had the best extraction effect.The optimal YOLO-v4 model had a detection accuracy of up to 97.78%and a recall rate of up to 98.16%on the dataset.The optimal YOLO-v4 model was compared with YOLO-v4,YOLO-v4 tiny,the YOLO-v3 model,and the Faster R-CNN model.With guaranteed accuracy,the recall rate was higher,up to 97.45%,and the detection of bayberry trees was better in different contexts.The result shows that the optimal YOLO-v4 model can accurately achieve the rapid detection and statistics of the number of bayberry trees in large-area orchards.
