An Improved Lightweight YOLOv5-Based Face Target Detection Algorithm

In this paper, an improved lightweight YOLOv5 based on a face target detection algorithm is proposed. First, build a small data set on top of the public data set. Next, the anchor frame is reset and the data annotation is standardized they improve the quality of the data set and improve the accuracy and speed of the model. The simulation results showed that the accuracy and mAP value of the improved YOLOv5 network reached 95.52% and 95.15%, respectively. They both have a 15% increase, and the computing speed has also increased by 19.00%. At last, this paper realizes faster and more accurate detection than the traditional YOLOv5 network.