A two-stage detection algorithm for abnormal behavior in smart construction site based on FCOS

Abstract

The existing classical object detection algorithm cannot satisfactorily detect abnormal behavior of operators 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 behavior markers by the FCOS， and then uses the MLP （multilayer perceptron） to detect and classify abnormal behavior. Finally， the 12，977 sample images collected from the relevant project sites are used as the data set to experimentally 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.

Keywords

• intelligent construction site
• abnormal behavior detection
• fcos
• mlp