针对船舶驾驶员值班过程中吸烟和打电话行为造成注意力分散威胁船舶航行安全的问题,提出一种基于改进RetinaFace和YOLOv4的吸烟和打电话行为检测算法。采用改进的RetinaFace网络提取人脸感兴趣区域,使用改进的YOLOv4目标检测模型检测该...针对船舶驾驶员值班过程中吸烟和打电话行为造成注意力分散威胁船舶航行安全的问题,提出一种基于改进RetinaFace和YOLOv4的吸烟和打电话行为检测算法。采用改进的RetinaFace网络提取人脸感兴趣区域,使用改进的YOLOv4目标检测模型检测该区域内是否存在香烟或手机,从而识别船舶驾驶员的吸烟和打电话行为。实验结果表明,本文算法具有较高的检测精度和速度,在自建数据集上的类平均精度(mean average precision,MAP)高达98.51%,误检率仅为3.2%。使用PyQt开发图形界面程序。该算法可以准确识别出驾驶员的吸烟和打电话行为,能够较好地适应船舶驾驶台的复杂环境,满足实时检测的要求。展开更多
针对在现有人脸静态识别过程中被识别人需等待配合的问题,文中提出了一种动态人脸识别系统。该系统采用了基于RetinaFace与FaceNet算法的动态人脸检测和识别方法,并进行了优化,以达到高识别精度和实时性的目标。其中,RetinaFace检测采用...针对在现有人脸静态识别过程中被识别人需等待配合的问题,文中提出了一种动态人脸识别系统。该系统采用了基于RetinaFace与FaceNet算法的动态人脸检测和识别方法,并进行了优化,以达到高识别精度和实时性的目标。其中,RetinaFace检测采用GhostNet作为骨干网络,使用Adaptive-NMS(Non Max Suppression)非极大值抑制用于人脸框的回归,FaceNet识别采用MobileNetV1作为骨干网络,使用Triplet损失与交叉熵损失结合的联合损失函数用以人脸分类。优化后的算法在检测与识别上具有良好表现,改进RetinaFace算法在WiderFace数据集下检测精度为93.35%、90.84%和80.43%,FPS(Frames Per Second)可达53 frame·s^(-1)。动态人脸检测平均检测精度为96%,FPS为21 frame·s^(-1)。当FaceNet阈值设为1.15时,识别率最高达到98.23%。动态识别系统平均识别精度98%,FPS可达20 frame·s^(-1)。实验结果表明,该系统解决了人脸静态识别中需等待配合的问题,具有较高的识别精度与实时性。展开更多
针对疲劳驾驶检测问题,提出一种基于人脸图像特征的眼部疲劳检测方法。利用RetinaFace网络检测面部区域的位置;通过级联回归树(ERT,Ensemble of Regression Trees)算法获取人脸68个关键特征点,同时完成对眼部区域的划分;计算人眼纵横比...针对疲劳驾驶检测问题,提出一种基于人脸图像特征的眼部疲劳检测方法。利用RetinaFace网络检测面部区域的位置;通过级联回归树(ERT,Ensemble of Regression Trees)算法获取人脸68个关键特征点,同时完成对眼部区域的划分;计算人眼纵横比,判断出睁眼和闭眼行为;根据PERCLOS度量准则实现疲劳状态的检测与判定。在YawDD数据集上的实验结果表明,该方法识别的平均准确率、精确率和召回率分别为90.24%、92.41%和91.90%,能有效识别眼部疲劳状态。展开更多
Cows’posture change is the fatal influencing factor for accurate identification of individual cows.To achieve non-contact,high-precision detection and identification of individual cows in farm environment,a cow indiv...Cows’posture change is the fatal influencing factor for accurate identification of individual cows.To achieve non-contact,high-precision detection and identification of individual cows in farm environment,a cow individual identification method by the fusion of RetinaFace and improved FaceNet was proposed.MobileNet-enhanced RetinaFace was applied to ameliorate the impact of output channel quantity and convolution kernel dynamics using depthwise convolution combined with pointwise convolution.Regression predictions of bovine facial features and keypoints were generated under varying distances,scales and sizes.FaceNet’s core feature network was enhanced through MobileNet integration,and the loss function was jointly optimized with Cross Entropy Loss and Triplet Loss to achieve a quicker and more stable convergence curve.The distances between the generated embedding vectors of cow facial features were corresponding to the similarity between cow faces,enabling accurate matching.RetinaFace exhibited detection false negative rates of 2.67%,0.66%,2.67%,and 3.33%under conditions of occlusion,no occlusion,low light,and bright light for cow facial detection.For cow facial pattern detection,the false negative rates for black and white patterns,pure black and pure white were 1.33%,6.00%and 8.00%,respectively.Regarding cow facial posture changes,the false negative rates for face upward,bowing down,profile,and normal posture were 1.33%,1.33%,4.00%and 0.66%,respectively.Improved FaceNet model achieved an accuray of 99.50%on training set and 83.60%on test set.In comparison to YOLOX,the recognition model presented in this research demonstrated increased accuracy in cow facial detection under occlusion,no occlusion and strong lighting conditions by 2.67%,0.40%,and 0.40%,respectively.Moreover,the accuracy for patterns with pure black and pure white tones surpassed that of YOLOX by 1.06%and 5.71%,correspondingly.Additionally,the accuracy rates for face upward,bowing down,profile and normal posture were higher than YOLOX by 2.00%,3.34%,2.66%and 0.40%,respectively.The proposed model demonstrates the proficiency in accurately identifying individual cows in natural scenes.展开更多
文摘针对船舶驾驶员值班过程中吸烟和打电话行为造成注意力分散威胁船舶航行安全的问题,提出一种基于改进RetinaFace和YOLOv4的吸烟和打电话行为检测算法。采用改进的RetinaFace网络提取人脸感兴趣区域,使用改进的YOLOv4目标检测模型检测该区域内是否存在香烟或手机,从而识别船舶驾驶员的吸烟和打电话行为。实验结果表明,本文算法具有较高的检测精度和速度,在自建数据集上的类平均精度(mean average precision,MAP)高达98.51%,误检率仅为3.2%。使用PyQt开发图形界面程序。该算法可以准确识别出驾驶员的吸烟和打电话行为,能够较好地适应船舶驾驶台的复杂环境,满足实时检测的要求。
文摘针对在现有人脸静态识别过程中被识别人需等待配合的问题,文中提出了一种动态人脸识别系统。该系统采用了基于RetinaFace与FaceNet算法的动态人脸检测和识别方法,并进行了优化,以达到高识别精度和实时性的目标。其中,RetinaFace检测采用GhostNet作为骨干网络,使用Adaptive-NMS(Non Max Suppression)非极大值抑制用于人脸框的回归,FaceNet识别采用MobileNetV1作为骨干网络,使用Triplet损失与交叉熵损失结合的联合损失函数用以人脸分类。优化后的算法在检测与识别上具有良好表现,改进RetinaFace算法在WiderFace数据集下检测精度为93.35%、90.84%和80.43%,FPS(Frames Per Second)可达53 frame·s^(-1)。动态人脸检测平均检测精度为96%,FPS为21 frame·s^(-1)。当FaceNet阈值设为1.15时,识别率最高达到98.23%。动态识别系统平均识别精度98%,FPS可达20 frame·s^(-1)。实验结果表明,该系统解决了人脸静态识别中需等待配合的问题,具有较高的识别精度与实时性。
文摘针对疲劳驾驶检测问题,提出一种基于人脸图像特征的眼部疲劳检测方法。利用RetinaFace网络检测面部区域的位置;通过级联回归树(ERT,Ensemble of Regression Trees)算法获取人脸68个关键特征点,同时完成对眼部区域的划分;计算人眼纵横比,判断出睁眼和闭眼行为;根据PERCLOS度量准则实现疲劳状态的检测与判定。在YawDD数据集上的实验结果表明,该方法识别的平均准确率、精确率和召回率分别为90.24%、92.41%和91.90%,能有效识别眼部疲劳状态。
基金supported by the National Natural Science Foundation of China(No.32272931)the Shaanxi Provincial Technology Innovation Guidance Planned Program(No.2022QFY11-02).
文摘Cows’posture change is the fatal influencing factor for accurate identification of individual cows.To achieve non-contact,high-precision detection and identification of individual cows in farm environment,a cow individual identification method by the fusion of RetinaFace and improved FaceNet was proposed.MobileNet-enhanced RetinaFace was applied to ameliorate the impact of output channel quantity and convolution kernel dynamics using depthwise convolution combined with pointwise convolution.Regression predictions of bovine facial features and keypoints were generated under varying distances,scales and sizes.FaceNet’s core feature network was enhanced through MobileNet integration,and the loss function was jointly optimized with Cross Entropy Loss and Triplet Loss to achieve a quicker and more stable convergence curve.The distances between the generated embedding vectors of cow facial features were corresponding to the similarity between cow faces,enabling accurate matching.RetinaFace exhibited detection false negative rates of 2.67%,0.66%,2.67%,and 3.33%under conditions of occlusion,no occlusion,low light,and bright light for cow facial detection.For cow facial pattern detection,the false negative rates for black and white patterns,pure black and pure white were 1.33%,6.00%and 8.00%,respectively.Regarding cow facial posture changes,the false negative rates for face upward,bowing down,profile,and normal posture were 1.33%,1.33%,4.00%and 0.66%,respectively.Improved FaceNet model achieved an accuray of 99.50%on training set and 83.60%on test set.In comparison to YOLOX,the recognition model presented in this research demonstrated increased accuracy in cow facial detection under occlusion,no occlusion and strong lighting conditions by 2.67%,0.40%,and 0.40%,respectively.Moreover,the accuracy for patterns with pure black and pure white tones surpassed that of YOLOX by 1.06%and 5.71%,correspondingly.Additionally,the accuracy rates for face upward,bowing down,profile and normal posture were higher than YOLOX by 2.00%,3.34%,2.66%and 0.40%,respectively.The proposed model demonstrates the proficiency in accurately identifying individual cows in natural scenes.
