Strategic Integration of Attention Modules in Object Detection: A Study on Regurgitation Echocardiography Dataset
學年 113
學期 2
發表日期 2025-04-22
作品名稱 Strategic Integration of Attention Modules in Object Detection: A Study on Regurgitation Echocardiography Dataset
作品名稱（其他語言）
著者 Shih-Hsin Chen; Yi-Hui Chen; HSIN-AN CHEN; CHENG-WEI TIEN; Yaro Imiye Franck Eleazar
作品所屬單位
出版者
會議名稱 The 11th IEEE International Conference on Applied System Innovation 2025
會議地點 Tokyo, Japan
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.
關鍵字 Echocardiography, Object Detection, YOLO, Attention Modules
語言 en
收錄於
會議性質 國際
校內研討會地點 無
研討會時間 20250422~20250425
通訊作者
國別 JPN
公開徵稿
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出處
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/126982 )