Design And Implementation Of Helmet Detection System Based On Invo-YOLOv5

The construction industry has become one of the most important pillar industries in China,but there is also a very high degree of danger.Construction workers are exposed to various hazards frequently,and not wearing helmets can lead to more safety accidents,so it is important to wear the helmet properly in construction sites.The previous manual inspection of helmets is time-consuming and labor-intensive,and the traditional sensorbased helmet detection has many limitations.With the rise of object detection,this field is applied to helmet detection.However,the existing source datasets are not abundant in quantity,which makes the study of helmet detection limited;Meanwhile,the size of the target to be detected in the monitoring range varies in practical application,coupled with the interference of factor such as scene complexity and occlusion,which brings us some great challenges to the helmet detection research;in addition,helmet detection often has some problems,such as missed detection,false detection,etc.Thus,leading to a decline in detection performance.Therefore,there is a need to further expand the safety helmet datasets and further improve the performance of object detection in the field of helmets detection.Firstly,a total of 10,124 helmet datasets are established,our new helmet datasets is quantitatively better than the existing open source helmet datasets and ensure the smooth progress of the subsequent work.Secondly,this paper proposes Invo-YOLOv5s model based on the shortcomings of the existing YOLOv5 model.Invo-YOLOv5s introduces Involution in the head module,while adding Shuffle Attention in Backbone,and using α-IoU Loss as the loss function.The Invo-YOLOv5s model was experimented on helmet datasets,and the experiments showed that Invo-YOLOv5s was chosen for training,and final obtained model detection accuracy reached 94.9%,which is a significant improvement over the original YOLOv5 model accuracy.Finally,the Invo-YOLOv5s is applied to the helmet detection system in this paper.The system is developed using Python language,and the UI interface is visualized using PyQt5.This system is fully functional and can achieve many functions such as the local image and the video detection,folder image batch detection and camera detection,which basically meets the actual needs in terms of functional completeness.Through a variety of testing,our system can realize the function of preliminary prediction,and it has a high anti-interference effect,which can well cope with the problem of wrong detection and omission caused by complex scenes in construction sites,etc.Moreover,the accuracy is high,and the performance can better meet the actual needs.