Research on helmet wear detection algorithm based on improved YOLOv5s

Which aims to address the shortcomings of current mainstream target detection methods in detecting small helmet-wearing targets, reducing the miss rate and improving detection accuracy. This work introduces a new target detection technique based on improved YOLOv5s.First, to increase the detection accuracy of the small target helmet, a 160×160 small target detection layer is added to the original model. Secondly, AFPN network structure is added to the neck part of YOLOv5s to carry out asymptotic multi-scale feature fusion to reduce information loss or degradation in multi-level transmission. Finally, in order to improve the EIoU loss function, inner ideas are presented., original function replaced by inner-EIoU, so as to improve the learning ability of samples under complex background. Experimental findings on the helmet dataset SHWD show that the improved algorithm model [email protected] and [email protected]:0.95 reach 95.4% and 62%, 1.7% and 1% over the YOLOv5s model, respectively, and has better detection effect.