Due to the complex environment of the university laboratory,personnel flow intensive,personnel irregular behavior is easy to cause security risks.Monitoring using mainstream detection algorithms suffers from low detec...Due to the complex environment of the university laboratory,personnel flow intensive,personnel irregular behavior is easy to cause security risks.Monitoring using mainstream detection algorithms suffers from low detection accuracy and slow speed.Therefore,the current management of personnel behavior mainly relies on institutional constraints,education and training,on-site supervision,etc.,which is time-consuming and ineffective.Given the above situation,this paper proposes an improved You Only Look Once version 7(YOLOv7)to achieve the purpose of quickly detecting irregular behaviors of laboratory personnel while ensuring high detection accuracy.First,to better capture the shape features of the target,deformable convolutional networks(DCN)is used in the backbone part of the model to replace the traditional convolution to improve the detection accuracy and speed.Second,to enhance the extraction of important features and suppress useless features,this paper proposes a new convolutional block attention module_efficient channel attention(CBAM_E)for embedding the neck network to improve the model’s ability to extract features from complex scenes.Finally,to reduce the influence of angle factor and bounding box regression accuracy,this paper proposes a newα-SCYLLA intersection over union(α-SIoU)instead of the complete intersection over union(CIoU),which improves the regression accuracy while increasing the convergence speed.Comparison experiments on public and homemade datasets show that the improved algorithm outperforms the original algorithm in all evaluation indexes,with an increase of 2.92%in the precision rate,4.14%in the recall rate,0.0356 in the weighted harmonic mean,3.60%in the mAP@0.5 value,and a reduction in the number of parameters and complexity.Compared with the mainstream algorithm,the improved algorithm has higher detection accuracy,faster convergence speed,and better actual recognition effect,indicating the effectiveness of the improved algorithm in this paper and its potential for practical application in laboratory scenarios.展开更多
为保证客舱安全,及时识别出旅客的异常行为,基于眼动仪,模拟客舱旅客异常行为,构建试验系统。选取参加过工作实习的空中保卫专业的学生作为被试,获取被试的视觉特征数据。基于多重分形消除趋势波动分析法(Multifractal Detrended Fluctu...为保证客舱安全,及时识别出旅客的异常行为,基于眼动仪,模拟客舱旅客异常行为,构建试验系统。选取参加过工作实习的空中保卫专业的学生作为被试,获取被试的视觉特征数据。基于多重分形消除趋势波动分析法(Multifractal Detrended Fluctuation Analysis of nonstationary time series,MF-DFA),分析航空安全员的视觉搜索特征。结果表明:航空安全员的注视持续时间和扫视幅度与其识别异常行为的能力存在显著的正相关关系;具有高识别能力的航空安全员的注视持续时间和扫视幅度奇异谱宽度均大于低识别能力的航空安全员的奇异谱宽度,具有较强的抗外界干扰能力。将MF-DFA算法引入航空安全员的视觉搜索特征分析,为航空安全员的培训和选拔等提供参考。展开更多
基金This study was supported by the National Natural Science Foundation of China(No.61861007)Guizhou ProvincialDepartment of Education Innovative Group Project(QianJiaohe KY[2021]012)Guizhou Science and Technology Plan Project(Guizhou Science Support[2023]General 412).
文摘Due to the complex environment of the university laboratory,personnel flow intensive,personnel irregular behavior is easy to cause security risks.Monitoring using mainstream detection algorithms suffers from low detection accuracy and slow speed.Therefore,the current management of personnel behavior mainly relies on institutional constraints,education and training,on-site supervision,etc.,which is time-consuming and ineffective.Given the above situation,this paper proposes an improved You Only Look Once version 7(YOLOv7)to achieve the purpose of quickly detecting irregular behaviors of laboratory personnel while ensuring high detection accuracy.First,to better capture the shape features of the target,deformable convolutional networks(DCN)is used in the backbone part of the model to replace the traditional convolution to improve the detection accuracy and speed.Second,to enhance the extraction of important features and suppress useless features,this paper proposes a new convolutional block attention module_efficient channel attention(CBAM_E)for embedding the neck network to improve the model’s ability to extract features from complex scenes.Finally,to reduce the influence of angle factor and bounding box regression accuracy,this paper proposes a newα-SCYLLA intersection over union(α-SIoU)instead of the complete intersection over union(CIoU),which improves the regression accuracy while increasing the convergence speed.Comparison experiments on public and homemade datasets show that the improved algorithm outperforms the original algorithm in all evaluation indexes,with an increase of 2.92%in the precision rate,4.14%in the recall rate,0.0356 in the weighted harmonic mean,3.60%in the mAP@0.5 value,and a reduction in the number of parameters and complexity.Compared with the mainstream algorithm,the improved algorithm has higher detection accuracy,faster convergence speed,and better actual recognition effect,indicating the effectiveness of the improved algorithm in this paper and its potential for practical application in laboratory scenarios.
文摘为保证客舱安全,及时识别出旅客的异常行为,基于眼动仪,模拟客舱旅客异常行为,构建试验系统。选取参加过工作实习的空中保卫专业的学生作为被试,获取被试的视觉特征数据。基于多重分形消除趋势波动分析法(Multifractal Detrended Fluctuation Analysis of nonstationary time series,MF-DFA),分析航空安全员的视觉搜索特征。结果表明:航空安全员的注视持续时间和扫视幅度与其识别异常行为的能力存在显著的正相关关系;具有高识别能力的航空安全员的注视持续时间和扫视幅度奇异谱宽度均大于低识别能力的航空安全员的奇异谱宽度,具有较强的抗外界干扰能力。将MF-DFA算法引入航空安全员的视觉搜索特征分析,为航空安全员的培训和选拔等提供参考。
