发电厂厂区内违规吸烟易导致火灾、爆炸等事故,会带来巨大损失;针对电厂内人员违规吸烟行为检测精度不高的问题,提出一种基于改进YOLOv5s(You Only Look Once v5s)的电厂内人员违规吸烟检测方法;该方法以YOLOv5s网络为基础,将YOLOv5s网...发电厂厂区内违规吸烟易导致火灾、爆炸等事故,会带来巨大损失;针对电厂内人员违规吸烟行为检测精度不高的问题,提出一种基于改进YOLOv5s(You Only Look Once v5s)的电厂内人员违规吸烟检测方法;该方法以YOLOv5s网络为基础,将YOLOv5s网络C3模块Bottleneck中的3×3卷积替换为多头自注意力层以提高算法的学习能力;接着在网络中添加ECA(Efficient Channel Attention)注意力模块,让网络更加关注待检测目标;同时将YOLOv5s网络的损失函数替换为SIoU(Scylla Intersection over Union),进一步提高算法的检测精度;最后采用加权双向特征金字塔网络(BiFPN,Bidirectional Feature Pyramid Network)代替原先YOLOv5s的特征金字塔网络,快速进行多尺度特征融合;实验结果表明,改进后算法吸烟行为的检测精度为89.3%,与改进前算法相比平均精度均值(mAP,mean Average Precision)提高了2.2%,检测效果显著提升,具有较高应用价值。展开更多
Smoking is the main reason for fire disaster and pollution in petrol station,construction site and warehouse.Existing solutions based on wearable devices and smoking sensors were costly and hard to obtain evidence of ...Smoking is the main reason for fire disaster and pollution in petrol station,construction site and warehouse.Existing solutions based on wearable devices and smoking sensors were costly and hard to obtain evidence of smoking in unmanned scenarios.With the developments of closed circuit television(CCTV)system,vision-based methods for object detection,mostly driven by deep learning techniques,were introduced recently.However,the massive GPU computing hardware required by the deep learning algorithm made these methods hard to be deployed.This paper aims at solving the smoking detection problem on edge and proposes the solution that has fast detection speed,high accuracy on micro-objects and low computing budget,i.e.,it could be deployed on the edge device such as NVIDIA JETSON TX2.We designed a new framework named RTVBS based on yolov3 and made a smoking dataset to train our model.We raised several methods to improve detection accuracy during the training step.The validation results show our model has excellent performance in smoking detection.展开更多
文摘发电厂厂区内违规吸烟易导致火灾、爆炸等事故,会带来巨大损失;针对电厂内人员违规吸烟行为检测精度不高的问题,提出一种基于改进YOLOv5s(You Only Look Once v5s)的电厂内人员违规吸烟检测方法;该方法以YOLOv5s网络为基础,将YOLOv5s网络C3模块Bottleneck中的3×3卷积替换为多头自注意力层以提高算法的学习能力;接着在网络中添加ECA(Efficient Channel Attention)注意力模块,让网络更加关注待检测目标;同时将YOLOv5s网络的损失函数替换为SIoU(Scylla Intersection over Union),进一步提高算法的检测精度;最后采用加权双向特征金字塔网络(BiFPN,Bidirectional Feature Pyramid Network)代替原先YOLOv5s的特征金字塔网络,快速进行多尺度特征融合;实验结果表明,改进后算法吸烟行为的检测精度为89.3%,与改进前算法相比平均精度均值(mAP,mean Average Precision)提高了2.2%,检测效果显著提升,具有较高应用价值。
文摘Smoking is the main reason for fire disaster and pollution in petrol station,construction site and warehouse.Existing solutions based on wearable devices and smoking sensors were costly and hard to obtain evidence of smoking in unmanned scenarios.With the developments of closed circuit television(CCTV)system,vision-based methods for object detection,mostly driven by deep learning techniques,were introduced recently.However,the massive GPU computing hardware required by the deep learning algorithm made these methods hard to be deployed.This paper aims at solving the smoking detection problem on edge and proposes the solution that has fast detection speed,high accuracy on micro-objects and low computing budget,i.e.,it could be deployed on the edge device such as NVIDIA JETSON TX2.We designed a new framework named RTVBS based on yolov3 and made a smoking dataset to train our model.We raised several methods to improve detection accuracy during the training step.The validation results show our model has excellent performance in smoking detection.
