Corn-associated weed detection model based on WAAP-YOLO
To address the challenges of corn-associated weed detection, such as diverse shapes, dense occlusion, complex backgrounds and scale variation, an improved object detection model, WAAP-YOLO, was proposed. First, the backbone was improved by replacing some convolutions with wavelet pooling convolution...
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| Format: | Article |
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Hebei University of Science and Technology
2025-08-01
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| Series: | Journal of Hebei University of Science and Technology |
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| Online Access: | https://xuebao.hebust.edu.cn/hbkjdx/article/pdf/b202504004?st=article_issue |
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| _version_ | 1849304138702127104 |
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| author | Zhiyong MENG Yawei JIA Xiuqing ZHANG Yongjing NI Ming ZHANG Qi WU Chenxi WU |
| author_facet | Zhiyong MENG Yawei JIA Xiuqing ZHANG Yongjing NI Ming ZHANG Qi WU Chenxi WU |
| author_sort | Zhiyong MENG |
| collection | DOAJ |
| description | To address the challenges of corn-associated weed detection, such as diverse shapes, dense occlusion, complex backgrounds and scale variation, an improved object detection model, WAAP-YOLO, was proposed. First, the backbone was improved by replacing some convolutions with wavelet pooling convolutions, effectively avoiding aliasing artifacts. Second, an aggregated attention mechanism was introduced to construct the C2f-AA module, improving the model′s ability to extract weed features in complex backgrounds. Finally, ASF-P2-Net was proposed to replace the original neck network, incorporating the P2 detection head through the scale sequence fusion module, reducing model complexity and significantly improving small object detection performance. Experimental results show that the WAAP-YOLO detection algorithm achieves 97.2% mAP@0.5, 85.8% mAP@0.5∶0.95, 94.0% F1 score, and a parameter count of 2.1×106, outperforming common object detection models such as YOLOv5s, YOLOv8n, and YOLOv10n. The proposed model can significantly enhance cornfield weed recognition accuracy, which provides some reference for advancing the intelligent and sustainable development of the agricultural industry. |
| format | Article |
| id | doaj-art-d9d002c172f24b2788d89bc887eee3de |
| institution | Kabale University |
| issn | 1008-1542 |
| language | zho |
| publishDate | 2025-08-01 |
| publisher | Hebei University of Science and Technology |
| record_format | Article |
| series | Journal of Hebei University of Science and Technology |
| spelling | doaj-art-d9d002c172f24b2788d89bc887eee3de2025-08-20T03:55:48ZzhoHebei University of Science and TechnologyJournal of Hebei University of Science and Technology1008-15422025-08-0146438639410.7535/hbkd.2025yx04004b202504004Corn-associated weed detection model based on WAAP-YOLOZhiyong MENG0Yawei JIA1Xiuqing ZHANG2Yongjing NI3Ming ZHANG4Qi WU5Chenxi WU6School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, ChinaSchool of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, ChinaSchool of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, ChinaSchool of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, ChinaSchool of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, ChinaSchool of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, ChinaSchool of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, ChinaTo address the challenges of corn-associated weed detection, such as diverse shapes, dense occlusion, complex backgrounds and scale variation, an improved object detection model, WAAP-YOLO, was proposed. First, the backbone was improved by replacing some convolutions with wavelet pooling convolutions, effectively avoiding aliasing artifacts. Second, an aggregated attention mechanism was introduced to construct the C2f-AA module, improving the model′s ability to extract weed features in complex backgrounds. Finally, ASF-P2-Net was proposed to replace the original neck network, incorporating the P2 detection head through the scale sequence fusion module, reducing model complexity and significantly improving small object detection performance. Experimental results show that the WAAP-YOLO detection algorithm achieves 97.2% mAP@0.5, 85.8% mAP@0.5∶0.95, 94.0% F1 score, and a parameter count of 2.1×106, outperforming common object detection models such as YOLOv5s, YOLOv8n, and YOLOv10n. The proposed model can significantly enhance cornfield weed recognition accuracy, which provides some reference for advancing the intelligent and sustainable development of the agricultural industry.https://xuebao.hebust.edu.cn/hbkjdx/article/pdf/b202504004?st=article_issuecomputer neural networks; weed recognition; wavelet pooling; attention mechanism; multi-scale fusion |
| spellingShingle | Zhiyong MENG Yawei JIA Xiuqing ZHANG Yongjing NI Ming ZHANG Qi WU Chenxi WU Corn-associated weed detection model based on WAAP-YOLO Journal of Hebei University of Science and Technology computer neural networks; weed recognition; wavelet pooling; attention mechanism; multi-scale fusion |
| title | Corn-associated weed detection model based on WAAP-YOLO |
| title_full | Corn-associated weed detection model based on WAAP-YOLO |
| title_fullStr | Corn-associated weed detection model based on WAAP-YOLO |
| title_full_unstemmed | Corn-associated weed detection model based on WAAP-YOLO |
| title_short | Corn-associated weed detection model based on WAAP-YOLO |
| title_sort | corn associated weed detection model based on waap yolo |
| topic | computer neural networks; weed recognition; wavelet pooling; attention mechanism; multi-scale fusion |
| url | https://xuebao.hebust.edu.cn/hbkjdx/article/pdf/b202504004?st=article_issue |
| work_keys_str_mv | AT zhiyongmeng cornassociatedweeddetectionmodelbasedonwaapyolo AT yaweijia cornassociatedweeddetectionmodelbasedonwaapyolo AT xiuqingzhang cornassociatedweeddetectionmodelbasedonwaapyolo AT yongjingni cornassociatedweeddetectionmodelbasedonwaapyolo AT mingzhang cornassociatedweeddetectionmodelbasedonwaapyolo AT qiwu cornassociatedweeddetectionmodelbasedonwaapyolo AT chenxiwu cornassociatedweeddetectionmodelbasedonwaapyolo |