基于深度学习的园区安防吸烟行为检测

Detection of smoking behavior in park security based on deep learning

针对园区禁烟区时常出现一些流动人员违规抽烟造成的安防隐患问题,提出一种联合人体骨骼关键点检测和改进的YOLOv7烟支检测的吸烟行为深度学习检测方法。该方法首先通过OpenPose提取人体关键点的坐标信息,计算手、鼻子、脖子之间距离的比值,手、肘、肩之间的角度,判别是否满足吸烟姿态。然后联合改进的YOLOv7算法检测图像中是否存在烟支,来最终判断吸烟行为是否存在。其中改进的YOLOv7算法引入了全局注意力机制模块,强化了语义与位置信息,使用转置卷积改进上采样方式,减小了信息丢失,并采用MPDIOU损失函数,增强了回归结果准确性,提升了对烟支小目标的检测精度。通过实验测试,本文方法准确率达到95.45%,可以有效地检测出吸烟行为。

Aiming at the hidden security risks caused by some mobile workers illegally smoking in no-smoking areas of the park,a deep learning method for smoking behavior detection combined with human bone key point detection and improved YOLOv7 cigarette detection was proposed.The method first extracts the coordinate information of the key points of the human body through OpenPose,calculates the ratio of the distance between the hand,nose and neck,and the angle between the hand,elbow and shoulder,and determines whether the smoking posture is met.Then,the improved YOLOv7 algorithm is combined to detect whether there is a cigarette in the image to finally determine whether there is smoking behavior.The improved YOLOv7 algorithm introduces a global attention mechanism module,strengthens the semantic and position information,uses transposed convolution to improve the upsampling method,reduces information loss,and adopts the MPDIOU loss function to enhance the accuracy of the regression results and improve the detection accuracy of small cigarette targets.Through experimental tests,the accuracy of this method reaches 95.45%,which can effectively detect smoking behavior.