融合人体关节点的安全帽佩戴检测

针对已有安全帽佩戴检测模型需求样本数据量大、易产生误检的问题,提出一种结合人体关节点检测和Faster R-CNN的安全帽佩戴检测模型;通过OpenPose从图像中定位人体头颈部位置并自动截取其周围小范围的子图像,然后利用Faster R-CNN检测子图像中的安全帽,最后分析安全帽中心点和头颈部节点之间的空间关系,进而判别是否正确佩戴安全帽。结果表明:相比传统目标检测方法,提出的增强检测方法有效降低了误检率,提高了环境适应性,同时,该方法在训练样本量较小时,召回率提高超过20%,准确率提高约10%,很大程度上减少了对训练样本的需求。

Helmet-wearing detection considering human joint

In order to address flaws of existing helmet-wearing detection model,such as its requirement of large sample data and inclination to false detection,a new detection model was proposed that combined human joint detection and Faster R-CNN.Then,OpenPose was utilized to locate positions of head and neck joints,and sub-image of small areas near helmet was extracted before it was detected with Faster R-CNN.Finally,spatial relationship between helmet and head/neck joints were analyzed to further verify whether it was worn correctly.The results show that this enhanced method can reduce error rate and improve its environmental adaptation effectively.And even with small sample data,its recall rate increases by more than 20%and detection accuracy by approximately 10%,significantly reducing demand on samples.