vbp -YOLO-剪枝:通过特征自适应融合和高效的YOLO剪枝,在可变天气下进行稳健的苹果检测

IF 6.8 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Haohai You , Hao Wang , Zhanchen Wei , Chunguang Bi , Lijuan Zhang , Xuefang Li , Yingying Yin
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引用次数: 0

摘要

摘苹果机器人在智能农业中的应用越来越多,但它们的性能受到复杂果园条件的限制,如光照不稳定、遮挡和天气变化。为了提高边缘设备的检测精度和部署效率,本研究提出了一种基于YOLOv8n的优化轻量级检测模型VBP-YOLO-prune。该模型结合了V7下采样模块、BiFPN特征融合和改进的PIOUv2损失函数,旨在改进多尺度表示和边界盒回归。一个定制的苹果数据集增加了不同的照明和天气条件,以提高泛化。10倍交叉验证的实验结果表明,VBP-YOLO-prune的mAP50和mAP50 - 95的准确率分别为89.0 %和66.26 %,精密度为84.01 %,召回率为80.52 %。此外,与YOLOv8n相比,它减少了79.7% %,FLOPs减少了60.9 %,FPS增加了29.2% %。最终模型仅包含0.61 M参数,3.2 GFLOPs,在NVIDIA Jetson Orin Nano上以102.6 FPS运行。这些结果表明,VBP-YOLO-prune为复杂环境下的水果实时检测提供了实用高效的解决方案。未来的研究可能会将这种方法扩展到其他作物类型,并探索将其完全集成到自主收获系统中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
VBP-YOLO-prune: Robust apple detection under variable weather via feature-adaptive fusion and efficient YOLO pruning
Apple-picking robots are increasingly applied in smart agriculture, but their performance is limited by complex orchard conditions such as unstable lighting, occlusion, and weather variations. This study proposes an optimized lightweight detection model, VBP-YOLO-prune, based on YOLOv8n, to enhance detection accuracy and deployment efficiency on edge devices. The model incorporates a V7 downsampling module, BiFPN feature fusion, and an improved PIOUv2 loss function, aiming to improve multi-scale representation and bounding box regression. A custom apple dataset was augmented with diverse lighting and weather conditions to improve generalization. Experimental results using 10-fold cross-validation show that VBP-YOLO-prune achieves 89.0 % mAP50 and 66.26 % mAP50–95, with Precision of 84.01 % and Recall of 80.52 %. Additionally, it reduces parameters by 79.7 %, FLOPs by 60.9 %, and increases FPS by 29.2 % compared to YOLOv8n.The final model contains only 0.61 M parameters, 3.2 GFLOPs, and runs at 102.6 FPS on NVIDIA Jetson Orin Nano. These results demonstrate that VBP-YOLO-prune provides a practical and efficient solution for real-time fruit detection in complex environments. Future research may extend this approach to other crop types and explore full integration into autonomous harvesting systems.
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来源期刊
alexandria engineering journal
alexandria engineering journal Engineering-General Engineering
CiteScore
11.20
自引率
4.40%
发文量
1015
审稿时长
43 days
期刊介绍: Alexandria Engineering Journal is an international journal devoted to publishing high quality papers in the field of engineering and applied science. Alexandria Engineering Journal is cited in the Engineering Information Services (EIS) and the Chemical Abstracts (CA). The papers published in Alexandria Engineering Journal are grouped into five sections, according to the following classification: • Mechanical, Production, Marine and Textile Engineering • Electrical Engineering, Computer Science and Nuclear Engineering • Civil and Architecture Engineering • Chemical Engineering and Applied Sciences • Environmental Engineering
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