Improvement methods for YOLO object detection algorithm targeting embedded devices
To address the problem of implementing algorithms on resource-limited embedded devices,a lightweight im-provement is proposed based on the YOLO series of algorithms to adapt to embedded device implementation,specif-ically including:improving the network backbone by introducing GhostNet ideas based on the YOLOv4-Tiny algo-rithm structure to significantly reduce network parameters and computational complexity;strengthening the fusion effect of neck network features to reduce accuracy loss caused by model compression;and using quantization during training to convert network model parameters from 32-bit floating-point data to 8-bit fixed-point parameters suitable for embedded device computation.Experimental results show that after the improvement in this paper,the network's model size relative to the original algorithm is reduced by 57％when the detection accuracy meets application re-quirements,and the power consumption for embedded device implementation is only 3.795 W.
国家科技期刊平台
NSTL
万方数据
维普
