基于YOLO的轻量化目标检测方法研究
Improved Lightweight Target Detection Based on YOLOv5
文磊1
作者信息
- 1. 中国电子科技集团公司第十研究所,四川成都 610036
- 折叠
摘要
针对移动端目标检测算法需要模型参数量与计算量更少、推理速度更快和检测效果更好以及目标检测算法对于小目标误检、漏检及特征提取能力不足等问题,提出一种基于YOLOv5改进的轻量化目标检测算法.该算法使用轻量级网络MobileNetV2作为目标检测算法的骨干网络降低模型的参数量与计算量,通过使用深度可分离卷积结合大卷积核的思想降低网络的计算量与参数量,并提升了小 目标的检测精度.使用GhostConv来替换部分普通卷积,进一步降低参数量与计算量.本文算法在VOC竞赛数据集,COCO竞赛数据集两份数据集上均进行了多次对比实验,结果表明本文算法相比于其他模型参数量更小、计算量更小、推理速度更快以及检测精度更高.
Abstract
Mobile target detection algorithms require fewer model parameters,less computation,faster reasoning speed,and better detec-tion effects.The target detection algorithms are prone to false detection of small targets and missing detection and have insufficient ability for feature extraction.To this end,this study proposes a lightweight small target detection algorithm based on YOLOv5.In this algorithm,the lightweight network MobileNetV2 is used as the backbone network of the target detection algorithm to reduce the number of parameters and calculation amount of the model.The deep separable convolution combined with a large convolution kernel is applied to decline the number of parameters and calculation amount,and improve the detection accuracy of small targets.GhostConv is adopted to replace part of com-monconvolution to further decrease the number of parameters and computation amount.Multiple comparison experiments are carried out on VOC competition data sets and COCO competition data sets.The results show that compared with other models,the proposed algorithm has fewer parameters,less computation,faster reasoning speed,and higher detection accuracy.
关键词
轻量化/深度学习/特征金字塔网络(FPN)/YOLOv5/大核卷积Key words
Lightweight/Deep learning/Feature pyramid network(FPN)/YOLOv5/Large kernel convolution引用本文复制引用
出版年
2024
NETL
NSTL
万方数据