New Plum Detection in Complex Environments Based on Improved YOLOv8n

文献类型： 外文期刊

第一作者： Chen, Xiaokang

作者： Chen, Xiaokang;Dong, Genggeng;Xu, Yan;Zou, Xiangjun;Zhou, Jianping;Jiang, Hong;Chen, Xiaokang;Dong, Genggeng;Xu, Yan;Zhou, Jianping;Jiang, Hong;Fan, Xiangpeng;Fan, Xiangpeng;Zou, Xiangjun

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关键词： new plum detection; YOLOv8n; attention mechanism; RFB module; deep learning

期刊名称：AGRONOMY-BASEL （ 影响因子：3.4； 五年影响因子：3.8 ）

ISSN：

年卷期： 2024 年 14 卷 12 期

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收录情况： SCI

摘要： To address the challenge of accurately detecting new plums amidst trunk and leaf occlusion and fruit overlap, this study presents a novel target detection model, YOLOv8n-CRS. A specialized dataset, specifically designed for new plums, was created under real orchard conditions, with the advanced YOLOv8n model serving as the base network. Initially, the CA attention mechanism was introduced to the backbone network to improve the model's ability to extract crucial features of new plums. Subsequently, the RFB module was incorporated into the neck layer to leverage multiscale information, mitigating inaccuracies caused by fruit overlap and thereby enhancing detection performance. Finally, the original CIOU loss function was replaced with the SIOU loss function to further enhance the model's detection accuracy. Test results show that the YOLOv8n-CRS model achieved a recall rate of 88.9%, with average precision scores of mAP@0.5 and mAP@0.5:0.95 recorded at 96.1% and 87.1%, respectively. The model's F1 score reached 90.0%, and it delivered a real-time detection speed of 88.5 frames per second. Compared to the YOLOv8n model, the YOLOv8n-CRS exhibited a 2.2-percentage-point improvement in recall rate, alongside increases of 0.7 percentage points and 1.2 percentage points in mAP@0.5 and mAP@0.5:0.95, respectively. In comparison to the Faster R-CNN, YOLOv4, YOLOv5s, and YOLOv7 models, the YOLOv8n-CRS model features the smallest size of 6.9 MB. This streamlined design meets the demands for real-time identification of new plums in intricate orchard settings, providing strong technical backing for the visual perception systems of advanced plum-picking robots.

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