自动化与信息工程2025，Vol.46Issue(1)：29-35,7.DOI:10.3969/j.issn.1674-2605.2025.01.004
基于改进YOLOv10的轻量化目标检测算法
Lightweight Object Detection Algorithm Based on Improved YOLOv10
刘印 1龚长友 2徐国栋1
作者信息
- 1. 西南林业大学,云南 昆明 650224
- 2. 新疆生产建设兵团兴新职业技术学院,新疆 铁门关 841007
- 折叠
摘要
Abstract
Aiming at the lightweight requirements of deploying object detection algorithms on edge devices,a lightweight object detection algorithm based on improved YOLOv10(CMD-YOLO algorithm)is proposed.This algorithm utilizes a cross-scale feature fusion module to improve the network structure of YOLOv10 algorithm,reducing the parameter and computational complexity of the algorithm model;Adopting a Mamba based linear attention mechanism to improve the partial self attention module and replace the traditional partial self attention module,further reducing the parameter count of the algorithm model;Replacing some traditional convolution modules with spatial depth conversion convolution modules enhances the algorithm model's ability to extract downsampling detail information;By using the dynamic UpSampler DySample to replace the traditional upsampling module,the computational delay of the algorithm model is reduced while maintaining upsampling accuracy.The experimental results show that compared with the YOLOv10-n algorithm,the CMD-YOLO algorithm has slightly improved detection accuracy,reduced model parameters by 30.5%,decreased computational complexity by 19%,reduced weight files by 29.3%,and reduced computational latency by 8.8%,which can meet the lightweight requirements of object detection algorithm deployment in edge devices.关键词
目标检测算法/YOLOv10算法/跨尺度特征融合模块/Mamba线性注意力机制/空间深度转换卷积模块/动态上采样器Key words
object detection algorithm/YOLOv10 algorithm/cross-scale feature fusion module/Mamba-like linear attention mechanism/space to depth Conv module/dynamic UpSampler分类
计算机与自动化引用本文复制引用
刘印,龚长友,徐国栋..基于改进YOLOv10的轻量化目标检测算法[J].自动化与信息工程,2025,46(1):29-35,7.
