摘要
对于智慧工地作业人员异常行为的检测,现有的经典目标检测算法无法到达理想的检测效果,准确率较低。为此,提出一种基于FCOS(全卷积单阶段目标检测)的二阶段检测算法来实现智慧工地异常行为检测。该算法主要包括两个级联网络,首先通过FCOS对作业人员及异常行为标志物进行识别定位,再使用MLP(多层感知器)完成异常行为的检测分类。最后以相关项目现场采集的12977张样本图片作为数据集,对检测算法进行实验验证。结果表明,该算法在对各类异常行为的检测中均表现优异,而且检测实时性好、计算复杂度低、模型参数少,在实际项目的部署及应用方面具有明显优势。
The existing classical object detection algorithm cannot satisfactorily detect abnormal behavior of opera⁃tors at smart construction sites for its low accuracy.To this end,a two-stage detection algorithm based on the FCOS(full convolutional single-stage target detection)is proposed to detect abnormal behavior at smart construction sites.The algorithm,mainly consisting of two cascaded networks,first identifies and locates operators and abnormal be⁃havior markers by the FCOS,and then uses the MLP(multilayer perceptron)to detect and classify abnormal behav⁃ior.Finally,the 12,977 sample images collected from the relevant project sites are used as the data set to experi⁃mentally validate the detection algorithm.The results show that the algorithm performs well in the detection of all kinds of abnormal behavior,and it has a clear advantage in the deployment and application of practical projects due to its excellent real-time detection,less complex computation,and fewer model parameters.
作者
朱强
孙晨
徐潘宇驰
闫云凤
ZHU Qiang;SUN Chen;XU Panyuchi;YAN Yunfeng(Infrastructure Department,Guangzhou Power Supply Bureau of Guangdong Power Grid Co.,Ltd.,Guangzhou 510050,China;College of Electrical Engineering,Zhejiang University,Hangzhou 310027,China)
出处
《浙江电力》
2023年第4期65-71,共7页
Zhejiang Electric Power
基金
国家自然科学基金项目(U1909201)
浙江省自然科学基金探索项目(Q21F030038)
浙江省科技“尖兵计划”项目(2022C01056)。
