Research on detection method of helmet wearing in construction site
Wearing a helmet at construction sites is a basic work norm in the construction industry in our country.Due to the complexity of the construction environment,the object in the pictures are numerous and chaotic,and the helmets occupy a relatively small proportion in the pictures.This makes it difficult to detect the wearing of helmets,resulting in a high rate of missed detection and false detection,and the detection precision is not very high.To improve the shortcomings of helmet detection,a helmet detection network called MFFMA-Net is proposed.This network is based on the YOLOX architecture and replaces the original PANET with a newly designed Multi-Scale Feature Fusion Module(MFFM).MFFM fuses features from two opposite directions based on feature map scales,which can include richer features extracted by the backbone network and improve the detection precision of helmet wearing.An attention module called FSI-ECA is added,which focuses spatial information into channel information based on full-pixel information,and fully utilizes channel and spatial information during the learning process.This module can fully utilize the feature information of helmets and reduce the missed detection and false detection rate of safety helmet wearing.Experimental results on the SHWD dataset show that the mAP of MFFMA-Net reaches 96.61%,which is 0.47% higher than YOLOX,and the Recall is improved by approximately 1% compared to YOLOX.The detection speed can reach 38 frames per second.It achieves high detection precision under real-time detection conditions and reduces the rate of missed detection and false detection.
NETL
NSTL
万方数据
