SaRPFF: A self-attention with register-based pyramid feature fusion module for enhanced rice leaf disease (RLD) detection.
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| Title: | SaRPFF: A self-attention with register-based pyramid feature fusion module for enhanced rice leaf disease (RLD) detection. |
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| Authors: | Haruna, Yunusa1 (AUTHOR) yunusa2k2@buaa.edu.cn, Qin, Shiyin1 (AUTHOR), Chukkol, Abdulrahman Hamman Adama2 (AUTHOR), Bello, Isah3 (AUTHOR), Lawan, Adamu1 (AUTHOR) |
| Source: | Multimedia Tools & Applications. Sep2025, Vol. 84 Issue 31, p38017-38043. 27p. |
| Subjects: | Rice diseases & pests, Object recognition (Computer vision), Computers in agriculture, Computer vision |
| Abstract: | Detecting objects across varying scales is still a challenge in computer vision, particularly in agricultural applications like Rice Leaf Disease (RLD) detection, where objects exhibit significant scale variations (SV). Conventional object detection (OD) like Faster R-CNN, SSD, and YOLO methods often fail to effectively address SV, leading to reduced accuracy and missed detections. To tackle this, we propose SaRPFF (Self-Attention with Register-based Pyramid Feature Fusion), a novel module designed to enhance multi-scale object detection. SaRPFF integrates 2D-Multi-Head Self-Attention (MHSA) with Register tokens, improving feature interpretability by mitigating artifacts within MHSA. Additionally, it integrates efficient attention atrous convolutions into the pyramid feature fusion and introduce a deconvolutional layer for refined up-sampling. We evaluate SaRPFF on YOLOv7 using the MRLD and COCO datasets. Our approach demonstrates a + 2.61 % improvement in Average Precision (AP) on the MRLD dataset compared to the baseline FPN method in YOLOv7. Furthermore, SaRPFF outperforms other FPN variants, including BiFPN, NAS-FPN, and PANET, showcasing its versatility and potential to advance OD techniques. This study highlights SaRPFF effectiveness in addressing SV challenges and its adaptability across FPN-based OD models. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Detecting objects across varying scales is still a challenge in computer vision, particularly in agricultural applications like Rice Leaf Disease (RLD) detection, where objects exhibit significant scale variations (SV). Conventional object detection (OD) like Faster R-CNN, SSD, and YOLO methods often fail to effectively address SV, leading to reduced accuracy and missed detections. To tackle this, we propose SaRPFF (Self-Attention with Register-based Pyramid Feature Fusion), a novel module designed to enhance multi-scale object detection. SaRPFF integrates 2D-Multi-Head Self-Attention (MHSA) with Register tokens, improving feature interpretability by mitigating artifacts within MHSA. Additionally, it integrates efficient attention atrous convolutions into the pyramid feature fusion and introduce a deconvolutional layer for refined up-sampling. We evaluate SaRPFF on YOLOv7 using the MRLD and COCO datasets. Our approach demonstrates a + 2.61 % improvement in Average Precision (AP) on the MRLD dataset compared to the baseline FPN method in YOLOv7. Furthermore, SaRPFF outperforms other FPN variants, including BiFPN, NAS-FPN, and PANET, showcasing its versatility and potential to advance OD techniques. This study highlights SaRPFF effectiveness in addressing SV challenges and its adaptability across FPN-based OD models. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 13807501 |
| DOI: | 10.1007/s11042-025-20696-3 |
