河北科技大学学报2025，Vol.46Issue(4)：386-394,9.DOI:10.7535/hbkd.2025yx04004
基于WAAP-YOLO的玉米伴生杂草检测模型
Corn-associated weed detection model based on WAAP-YOLO
摘要
Abstract
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.关键词
计算机神经网络/杂草识别/小波池化/注意力机制/多尺度融合Key words
computer neural networks/weed recognition/wavelet pooling/attention mechanism/multi-scale fusion分类
信息技术与安全科学引用本文复制引用
孟志永,贾雅微,张秀清,倪永婧,张明,武琪,吴晨曦..基于WAAP-YOLO的玉米伴生杂草检测模型[J].河北科技大学学报,2025,46(4):386-394,9.基金项目
国家自然科学基金(62105093) （62105093）
石家庄市科技计划项目(241130291A) （241130291A）
河北省高等学校科学研究项目(CXZX2025046) （CXZX2025046）
河北省军民融合发展研究项目(HB24JMRH034) （HB24JMRH034）
