Object detection compression algorithm based on feature enhanced knowledge distillation
An object detection compression algorithm(ODCA)based on feature enhancement knowledge distillation is proposed.Firstly,coordinate attention is used to enhance the representation ability of intermediate layer features of the teacher network for foreground target.Then,binary masks are used to spatially distinguish foreground and background,and a weighted spatial information loss is defined to balance the strength of foreground and background feature distillation.Finally,the convolution operation is used to unify the number of network channels of teachers and students,and the distribution information between channels of the learning features of channel information loss is constructed.Experimental results on the Pascal VOC dataset show that the proposed algorithm improves the mean average precision(mAP)of the student network from 68.3%to 75.7%.
deep learningobject detectionmodel compressionknowledge distillationYOLOv5
NETL
NSTL
万方数据
