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 map@0.5 and map@0.5:0.95 reach 95.4% and 62%, 1.7% and 1% over the YOLOv5s model, respectively, and has better detection effect.
The main task of object detection is the recognition and localisation of objects in images. It is the cornerstone of image understanding and the preparation for vision tasks such as image segmentation, target tracking and pose recognition. YOLOv5 is a object detection technique that is widely used in military, medical and traffic applications. However, in complex and changing real-world scenes, there are often multiple interfering factors, so the detection effect of YOLOv5 is greatly affected. In this paper, we propose an improved YOLOv5 algorithm for complex scenes from the perspectives of background information interference and inaccurate bounding box localisation. Firstly, the SimAM attention module is added to YOLOv5 to enable the model to focus on the differences in information in the feature maps and to enhance the anti-interference ability of the network; then the EIoU loss is used in the loss function and the Focal EIoU loss function is proposed by combining the optimization idea of Focal loss to deal with the category imbalance and improve the convergence speed of the algorithm; finally ,a comparison experiment is conducted on the PASCAL VOC dataset to demonstrate the reliability of the improved algorithm.
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