基于ShuffleAttention相似目标检测

——以 SA-YOLOv7为例

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中图分类号：TP391.4；TP183 文献标识码：A 文章编号：2096-4706（2025）11-0106-08

Similar Object Detection Based on Shuffle Attention

-Taking SA-YOLOv7 asan Example

REN Yuzhen1，FAN Zhongkui1，FENG Zhenying²，ZHU Mei1 （1.SchoolofSoftwareEnginering，JangxiUniversityofScienceandTechnology，Nanchang33013，China;2.Guangdong Nikola Energy Technology Co.，Ltd.， Guangzhou 51070o， China）

Abstract： YOLOv7achieves excellent results in object detection，but there is stilla problemofhigh false detectionrate insimilarobjectdetection.ThemainreasonisthatYOLOv7hasinsuffcientabilitytoextractfine-rainedfeatures.Tosolvethe above problems，thisstudyproposesaSA-YOLOv7bectdetectionnetwork.Specificallywithoutchangingtheoverallstructure ofELAN，theatentionmoduleSAismergedwithittofoaSA-ELANmoduletoobtainmorechannelandspatialfeature information，therebyimprovingthedetectionaccuracyofsimilarobjects.The modelconductsalargenumberofcomparative experiments onpublichandand glovesimilarobjectdatasets，explores theinfluenceof thenumberand positionofSAadedto theYOLOv7 network ontheresults.Italsoreveals the underlying principleofSA'srole and deepens the understandingof the Attention Mechanism.The experimental results show that the detection accuracy of SA-YOLOv7 is 7.7% higher than that of YOLOv7， and its mAP@0.5：0.95 is 1.8% higher than that of YOLOv7. Compared with the latest YOLOvl1， it also has a 0.9% （2 detection accuracy advantage.TheresearchonSA-YOLOv7 provides assistance for thedevelopmentofsimilar objectdetection technology.

Keywords： Deep Learning; YOLOv7; Shufle Attention; similar Object Detection

0 引言

随着深度学习的发展，机器视觉取得了长足进步，目标检测作为机器视觉的重要研究方向，涌现出众多优秀算法[，它们在ImageNet、COCO、CIFAR-100等知名数据集上取得了优异的检测结果。（剩余12853字）