A Dense Multi-face Detection Based on Improved YOLOv8s
Aiming at the problems of high missed detection rate and low detection accuracy in dense multi-face detection,an improved face detection algorithm based on YOLOv8s is proposed.The SimAM attention mechanism is incorporated into the backbone network of YOLOv8 to enhance the feature extraction ability of the detection model for small targets in the images.The original SiLU activation function is replaced with the FReLU function to expand the range of feature point extraction and improve the accuracy of small object detection.A new loss function Wise-IoUv1 is introduced to solve the low-quality problem that may occur during the interception process of some small targets,and further improve the detection accuracy.The experimental results show that the improved algorithm can achieve an accuracy improvement up to 99.26%on the self-built face data set in dense background without significant increase in computational parameters compared with flat regression rates.It can reduce 26%missed detection rate,enhancing face detection capability effectively.
NETL
NSTL
万方数据
