A Safety Helmet Wearing Detection System Based On Deep Learning

Construction workers are still required to wear safety helmets in large-scale construction zones,as determined by manual inspection.This type of supervision has high personnel costs and low efficiency,and it has entirely failed to satisfy the demands of the construction industry’s rapid growth.Accidents caused by not wearing a helmet account for more than 60%of all accidents in the construction business in my nation in recent years.With the advancement of target detection technologies and smart cameras,intelligent helmet wearing detection is now achievable.This study examines popular target detection algorithms,weighs detection accuracy and speed,and chooses YOLOv4 as the foundation for a helmet-wearing detection method.YOLOv4 is upgraded,and a new helmet wearing detection algorithm is developed,in order to address the difficulties of YOLOv4’s low detection accuracy for small targets and difficulty in detecting substantially obscured targets.To model the interdependence between the feature channels extracted by the convolutional neural network,adaptively adjust the feature response value of each channel,and improve the model,the multi-spectral channel attention mechanism is first introduced into the backbone feature extraction network of YOLOv4.Second,YOLOv4’s path aggregation network and feature pyramid network structure have been improved,and the backbone feature extraction network’s low-resolution,fine-grained feature-rich shallow features and semantic information-rich deep features have been improved.Finally,the loss function is improved to reduce the weight of a large number of simple negative samples in training.Fusion,the output feature map with higher resolution,detects small targets;finally,the loss function is improved to reduce the weight of a large number of simple negative samples in training.The AP value and mAP value of each category are utilized as evaluation indicators,and a pairwise fusion experiment research is conducted on the model’s improvement points in order to produce the optimal model.Finally,the usefulness of the modified algorithm in this study is confirmed through extensive experimental research and actual picture testing.The mAP value of YOLOv4 has grown from 80.90 percent to 90.11 percent following the above three changes,and the upgraded model has a superior detection impact after passing the actual photo test.The trained model is transplanted to Huawei software-defined cameras through operations such as conversion,packaging,and signature,forming a complete set of Safety helmet wearing detection system,in order to apply the improved algorithm to the actual construction site,through secondary development of Huawei software-defined cameras.