Research on lightweight neural network algorithm based on improved YOLOv3
SHU Jun1， WU Ke1， LEI Jianjun2
1.Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System， Hubei University of Technology， Wuhan 430068， China；2.Hubei Co-Innovation Center of Basic Education Information Technology Services， Hubei University of Education， Wuhan 430205， China
Abstract：For the small sample datasets， the YOLOv3 neural network framework has problems of low feature utilization and low feature transfer efficiency during training， so its network performance is not fully utilized. To solve these problems， this paper proposed an improved YOLOv3 lightweight neural network，which changes the ResNet residual network structure in the YOLOv3 infrastructure to DenseNet's dense tandem structure and reduces the multi-scale output structure to two. Experiments showed that the FPS of the improved YOLOv3 neural network increased by 119.03% and the mAP-50 increased by 2.45%on the homemade mahjong dataset. Extendeding the improved model to the open source datasets like Kaggle and Caltech， the FPS of the improved model increased by 124.39% and 140.05% respectivelywhen compared with the original model， and mAP-50 increased by 12.5% and 5.34%respectively.
2021, Vol. 55 
