基于WAAP-YOLO的玉米伴生雜草檢測模型

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中圖分類號：TP391.41 文獻標識碼：A DOI：10.7535/hbkd.2025yx04004

Corn-associated weed detection model based on WAAP-YOLO

MENG Zhiyong，JIA Yawei， ZHANG Xiuqing， NI Yongjing， ZHANG Ming，WU Qi， WU Chenxi （School of Information Science and Engineering，Hebei University of Science and Technology， Shijiazhuang，HebeiO50ol8，China）

Abstract：Toaddress the challenges of corn-associated weed detection，such as diverse shapes，dense occusion，complex backgrounds and scale variation，animproved object detection model，WAAP-YOLO，was proposed.First，the backbone was improved byreplacing someconvolutions with wavelet poling convolutions，effectively avoiding aliasingartifacts.Second，an agregatedatentionmechanism wasintroduced toconstructtheC2f-AA module，improving the model'sabilitytoextractwed featuresincomplexbackgrounds.Finall，ASF-P2-Netwas proposed toreplace theoriginal neck network，incorporating the P2 detection head through the scale sequence fusion module，reducing model complexityand significantly improving small object detection performance. Experimental results show that the WAAP-YOLO detection algorithm achieves 97.2% mAP @ （2 0.5， 85.8% mAP@0.5：0.95 ，94. 0% Fl score，and a parameter count of 2.1×106 ，outperforming common object detection models such as YOLOv5s，YOLOv8n，and YOLOvlOn.The proposed model can significantly enhance cornfield weed recognitionacuracy，which providessomereferenceforadvancing theintellgent and sustainable developmentof the

agricultural industry.

Keywords：computer neural networks；weed recognition；wavelet pooling；attention mechanism； multi-scale fusio

玉米作為全球三大糧食作物之一，其產(chǎn)量至關重要，而雜草是限制其產(chǎn)量的主要因素之一[1]。（剩余12608字）