计算机应用与软件2025，Vol.42Issue(6)：218-224,240,8.DOI:10.3969/j.issn.1000-386x.2025.06.028
嵌入自适应空间注意力的Scaled-YOLOv4小目标检测模型
SCALED-YOLOV4 MODEL EMBEDDED WITH ADAPTIVE SPATIAL ATTENTION FOR SMALL OBJECT DETECTION
摘要
Abstract
In order to solve the problem of low detection accuracy caused by fixed receptive field in object detection while convolution only pays attention to conventional size targets and ignores the characteristics of small targets,an adaptive spatial attention mechanism is proposed.This method added parallel convolution kernels of different sizes and was embedded in the 3×3 convolution layer of Scaled-YOLOv4 residual structure,so that the network could adjust the receptive field size according to different sizes to enhance the feature extraction of small targets.The experimental results show that the new network model can effectively improve the detection accuracy of the algorithm for small targets,and improve the problems of false detection and missed detection in the original model.The detection accuracy on datasets such as MSCOCO and PASCAL VOC has been greatly improved.关键词
小目标检测/Scaled-YOLOv4/深度学习/注意力机制/自适应感受野Key words
Small object detection/Scaled-YOLOv4/Deep learning/Attention mechanism/Adaptive receptive field分类
信息技术与安全科学引用本文复制引用
张家源,窦全胜,唐焕玲..嵌入自适应空间注意力的Scaled-YOLOv4小目标检测模型[J].计算机应用与软件,2025,42(6):218-224,240,8.基金项目
国家自然科学基金项目(61976125,61976124) （61976125,61976124）
烟台市重点研发计划项目(2019XDHZ081,2017ZH065). （2019XDHZ081,2017ZH065）
