期刊論文
| 學年 | 113 |
|---|---|
| 學期 | 1 |
| 出版（發表）日期 | 2024-08-13 |
| 作品名稱 | SW-YOLOX: A YOLOX-based real-time pedestrian detector with shift window-mixed attention mechanism |
| 作品名稱（其他語言） | |
| 著者 | Chi-Yi Tsai; Run-Yu Wang; Yu-Chen Chiu |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | Neurocomputing, Vol. 606, No. 128357, p. 1-16 |
| 摘要 | Pedestrian detection is a critical research area in computer vision with practical applications. This paper addresses this key topic by providing a novel lightweight model named Shift Window-YOLOX (SW-YOLOX). The purpose of SW-YOLOX is to significantly enhance the robustness and real-time performance of pedestrian detection under practical application requirements. The proposed method incorporates a novel Shift Window-Mixed Attention Mechanism (SW-MAM), which combines spatial and channel attention for effective feature extraction. In addition, we introduce a novel up-sampling layer, PatchExpandingv2, to enhance spatial feature representation while maintaining computational efficiency. Furthermore, we propose a novel Shift Window-Path Aggregation Feature Pyramid Network (SW-PAFPN) to integrate with the YOLOX detector, further enhancing feature extraction and the robustness of pedestrian detection. Experimental results validated on challenging datasets such as CrowdHuman, MOT17Det, and MOT20Det demonstrate the competitive performance of the proposed SW-YOLOX compared to state-of-the-art methods and its pedestrian detection performance in crowded and complex scenes. |
| 關鍵字 | |
| 語言 | en_US |
| ISSN | |
| 期刊性質 | 國外 |
| 收錄於 | SCI EI |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 是 |
| 國別 | USA |
| 公開徵稿 | |
| 出版型式 | ,電子版,紙本 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/126315 ) |