中文摘要：

安全帽在工业生产中的应用非常广泛，为了防止事故发生，确保生产安全，建立对安全帽的自动检测及报警系统变得越来越迫切。主要对安全帽的识别算法进行了研究，采用肤色检测的方法定位到人脸区域，并以此获得脸部以上的区域图像，将Hu矩作为图像的特征向量，分别比较神经网络和支持向量机（SVM）两种分类模型。实验结果表明：SVM对安全帽的识别有很好的效果，将会对监控系统实现智能化提供有力的支持和实际的指导意义。

英文摘要：

Helmet has been widely used in industrial production. In order to prevent accidents and ensure production safety, it is quite important to set up an automatic detecting and warning system. This paper focuses on the recognition algorithm on helmet. By using skin color detection to locate the face region, the region images above the face can be obtained. Moreover, Hu moments are taken as image feature vectors to compare neural network and SVM as classification models. Experimental results show that SVM can attain a better recognition on helmet and provides a stronger support and practical guidance for the realization of intelligent monitoring system.