【摘要】 针对目前电力施工人员着装检测算法在实际作业场景中适应性差、误检率高等问题，文章提出一种结合Alphapose和ResNet的电力施工人员着装检测模型。该模型通过Alphapose人体姿态估计模型得到施工视频中人员的关键点坐标，然后采用合适的裁剪算法根据坐标裁剪出所需的身体区域，最后应用改进的ResNet分类模型进行着装检测，判断工作人员是否穿戴安全帽、工作服及工作靴。该文章使用构建的数据集进行测试，结果表明：该方法准确率高，误检率低，能够满足实时性的需求，适合应用在实际的作业现场中。更多还原

【Abstract】 Aiming at the problems of poor adaptability and high false detection rate of the existing power facility staff’s dress detection algorithm in actual operation scenarios,this paper proposes a dress detection model for power construction personnel combining Alphapose and ResNet.The model obtains the key point coordinates of people in the construction video through the Alphapose human pose estimation model,then adopts the appropriate cropping algorithm to cut out the required body area according to the coordinates,and finally feeds it into the improved ResNet classification model for dress detection and judgment work whether people wear safety helmets,work clothes and work boots.We use the data set constructed by ourselves to conduct experiments.The experimental results show that the method has high accuracy and low false detection rate,can meet the needs of real-time,and is suitable for application in the actual job site.更多还原