计算机工程2025，Vol.51Issue(6)：65-73,9.DOI:10.19678/j.issn.1000-3428.0068540
基于大内核自适应融合的小目标检测算法
Small Object Detection Algorithm Based on Large Kernel Adaptive Fusion
摘要
Abstract
To address the challenges faced by current single-stage object detection algorithms based on convolutional neural networks(such as the YOLO series and VFNet)in high-altitude aerial shooting scenarios-including complex backgrounds,low detection accuracy,and feature overlap,this study proposes an end-to-end object detection algorithm called CSPENet.First,a deep convolutional network,CSPNeXt,with large kernels is used as the model's backbone,enhancing its capability to capture global context.Second,by introducing a Feature Refinement Module(FRM)in both spatial and channel dimensions,adaptive weights are generated that can effectively suppress overlapping features are generated.It adds a Receptive Field Attention(RFA)mechanism,based on mobile networks in the feature fusion stage to solve the problem of large kernel parameter sharing.Finally,the Efficient Intersection over Union(EIoU)loss function is utilized as the model's regression loss,separating the influencing factors of the aspect ratios between the predicted and ground truth boxes,which leads to faster convergence and improved localization accuracy.Experimental results demonstrate that CSPENet achieves an average accuracy improvement of 4.4 percentage points compared with the DINO algorithm on the VisDrone-DET dataset,offering a novel solution for research and applications in small object detection algorithms.关键词
大内核/小目标/上下文信息/特征细化/自适应融合/感受野Key words
large kernel/small object/contextual information/feature refinement/adaptive fusion/receptive field分类
计算机与自动化引用本文复制引用
王磊,胡君红,任洋..基于大内核自适应融合的小目标检测算法[J].计算机工程,2025,51(6):65-73,9.基金项目
国家自然科学基金(60101204) （60101204）
湖北省自然科学基金(2020CFB474). （2020CFB474）
