Strategic Integration of Attention Modules in Object Detection: A Study on Regurgitation Echocardiography Dataset

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題名:	Strategic Integration of Attention Modules in Object Detection: A Study on Regurgitation Echocardiography Dataset
作者:	Chen, Shih-Hsin;Chen, Yi-Hui;CHEN, HSIN-AN;TIEN, CHENG-WEI;Eleazar, Yaro Imiye Franck
關鍵詞:	Echocardiography, Object Detection, YOLO, Attention Modules
日期:	2025-04-22
上傳時間:	2025-03-20 12:05:20 (UTC+8)
摘要:	Several attention modules—such as SENet, CBAM, and SimAM—have been successfully applied in image classification tasks and could be integrated into object detection frameworks like YOLOv5, YOLOv7, and YOLOv9. However, the optimal insertion point within these detection architectures—whether in the backbone, neck, or head—remains an open question. In this study, we systematically investigate the effects of incorporating attention modules at various network locations. Experiments conducted on a regurgitation dataset of echocardiography images demonstrate that strategically inserting attention modules significantly improves performance, as measured by the mAP50 metric. Notably, the CBAM module proves particularly effective for the task at hand.
顯示於類別:	[資訊工程學系暨研究所] 會議論文

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