Detecting individuals wearing safety helmets in complex environments faces several challenges.These factors include limited detection accuracy and frequent missed or false detections.Additionally,existing algorithms o...Detecting individuals wearing safety helmets in complex environments faces several challenges.These factors include limited detection accuracy and frequent missed or false detections.Additionally,existing algorithms often have excessive parameter counts,complex network structures,and high computational demands.These challenges make it difficult to deploy such models efficiently on resource-constrained devices like embedded systems.Aiming at this problem,this research proposes an optimized and lightweight solution called FGP-YOLOv8,an improved version of YOLOv8n.The YOLOv8 backbone network is replaced with the FasterNet model to reduce parameters and computational demands while local convolution layers are added.This modification minimizes computational costs with only a minor impact on accuracy.A new GSTA(GSConv-Triplet Attention)module is introduced to enhance feature fusion and reduce computational complexity.This is achieved using attention weights generated from dimensional interactions within the feature map.Additionally,the ParNet-C2f module replaces the original C2f(CSP Bottleneck with 2 Convolutions)module,improving feature extraction for safety helmets of various shapes and sizes.The CIoU(Complete-IoU)is replaced with the WIoU(Wise-IoU)to boost performance further,enhancing detection accuracy and generalization capabilities.Experimental results validate the improvements.The proposedmodel reduces the parameter count by 19.9% and the computational load by 18.5%.At the same time,mAP(mean average precision)increases by 2.3%,and precision improves by 1.2%.These results demonstrate the model’s robust performance in detecting safety helmets across diverse environments.展开更多
基金funded by National Natural Science Foundation of China(61741303)the Foundation Project of Guangxi Key Laboratory of Spatial Information andMapping(No.21-238-21-16).
文摘Detecting individuals wearing safety helmets in complex environments faces several challenges.These factors include limited detection accuracy and frequent missed or false detections.Additionally,existing algorithms often have excessive parameter counts,complex network structures,and high computational demands.These challenges make it difficult to deploy such models efficiently on resource-constrained devices like embedded systems.Aiming at this problem,this research proposes an optimized and lightweight solution called FGP-YOLOv8,an improved version of YOLOv8n.The YOLOv8 backbone network is replaced with the FasterNet model to reduce parameters and computational demands while local convolution layers are added.This modification minimizes computational costs with only a minor impact on accuracy.A new GSTA(GSConv-Triplet Attention)module is introduced to enhance feature fusion and reduce computational complexity.This is achieved using attention weights generated from dimensional interactions within the feature map.Additionally,the ParNet-C2f module replaces the original C2f(CSP Bottleneck with 2 Convolutions)module,improving feature extraction for safety helmets of various shapes and sizes.The CIoU(Complete-IoU)is replaced with the WIoU(Wise-IoU)to boost performance further,enhancing detection accuracy and generalization capabilities.Experimental results validate the improvements.The proposedmodel reduces the parameter count by 19.9% and the computational load by 18.5%.At the same time,mAP(mean average precision)increases by 2.3%,and precision improves by 1.2%.These results demonstrate the model’s robust performance in detecting safety helmets across diverse environments.
