外文摘要：During the process of walnut identification and counting using UAVs in hilly areas, the complex lighting conditions on the surface of walnuts somewhat affect the detection effectiveness of deep learning models. To address this issue, we proposed a lightweight walnut small object recognition method called w-YOLO. We reconstructed the feature extraction network and feature fusion network of the model to reduce the volume and complexity of the model. Additionally, to improve the recognition accuracy of walnut objects under complex lighting conditions, we adopted an attention mechanism detection layer and redesigned a set of detection heads more suitable for walnut small objects. A series of experiments showed that when identifying walnut objects in UAV remote sensing images, w-YOLO outperforms other mainstream object detection models, achieving a mean Average Precision (mAP0.5) of 97% and an F1-score of 92%, with parameters reduced by 52.3% compared to the YOLOv8s model. Effectively addressed the identification of walnut targets in Yunnan, China, under the influence of complex lighting conditions.
外文关键词：walnut;low-altitude remote sensing;small object detection;YOLOv8s
作者：Wu, Mingjie;Yun, Lijun;Xue, Chen;Chen, Zaiqing;Xia, Yuelong
作者单位：Yunnan Normal Univ;Engn Res Ctr Comp Vis & Intelligent Control Techno
期刊名称：AGRICULTURE-BASEL
期刊影响因子：0.0
出版年份：2024
出版刊次：14（4）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台