Walnut Recognition Method for UAV Remote Sensing Images

Mingjie Wu, Lijun Yun (), Chen Xue, Zaiqing Chen and Yuelong Xia
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Mingjie Wu: School of Information, Yunnan Normal University, Kunming 650500, China
Lijun Yun: School of Information, Yunnan Normal University, Kunming 650500, China
Chen Xue: School of Information, Yunnan Normal University, Kunming 650500, China
Zaiqing Chen: School of Information, Yunnan Normal University, Kunming 650500, China
Yuelong Xia: School of Information, Yunnan Normal University, Kunming 650500, China

Agriculture, 2024, vol. 14, issue 4, 1-19

Abstract: 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.

Keywords: low-altitude remote sensing; walnut; small object detection; YOLOv8s (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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