广东工业大学学报2026，Vol.43Issue(2)：12-20,9.DOI:10.12052/gdutxb.240163
SCA-YOLO:基于可变形卷积与上下文感知注意力的实时目标检测
SCA-YOLO:Real-time Target Detection Based on Deformable Convolution and Context-aware Attention
摘要
Abstract
The YOLO(You Only Look Once)-based algorithms have been widely used for real-time object detection and achieved promising performance.However,this performance improvement still faces two challenges.First,standard convolutions with limited receptive fields are hard to capture global contextual features,reducing the detection accuracy of complex objects;Second,increasing the convolution kernel size can enhance feature extraction while the computational cost is significantly increased.To address these issues,this paper investigates the SCA-YOLO model by introducing the Alterable Channel-wise Fusion module(C2fAK)and the Context-Aware Attention++(CAA++)module to enhance performance.The C2fAK module combines deformable convolution with the Channel-wise Fusion(C2f)structure to enhance feature representation capability while balancing computational overhead.The CAA++module captures long-range contextual information and reduces channel redundancy,further improving detection accuracy.Experimental results show that the proposed SCA-YOLO outperforms existing methods on multiple datasets,demonstrating its effectiveness and efficiency in object detection.关键词
YOLO/目标检测/可变形卷积/注意力Key words
YOLO(You Only Look Once)/object detection/deformable convolution/attention mechanism分类
信息技术与安全科学引用本文复制引用
邓立国,沙文丹..SCA-YOLO:基于可变形卷积与上下文感知注意力的实时目标检测[J].广东工业大学学报,2026,43(2):12-20,9.基金项目
国家自然科学基金青年基金资助项目(62072120) （62072120）
