基于改进的 yolov5 的苹果幼果检测方法

Yonghui Du, Ang Gao, Yuepeng SONG, Jing Guo, Wei Ma, Longlong Ren
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引用次数: 0

摘要

基于深度学习的苹果幼果智能检测面临着各种挑战,如尺度大小不一、颜色与背景相似等,这增加了误检或漏检的风险。为有效解决这些问题,本文提出了一种基于改进型 YOLOv5 的苹果幼果检测方法。首先,建立了苹果幼果数据集。随后,在模型的检测头中添加了预测层,并在检测颈(Neck)中集成了四层 CA 注意机制。此外,还引入了 GIOU 函数作为模型的损失函数,以提高其整体检测性能。验证数据集的准确率达到 94.6%,平均精度为 82.2%。与 YOLOv3、YOLOv4 和原有的 YOLOv5 检测方法相比,准确率分别提高了 0.4%、1.3% 和 4.6%,平均精度分别提高了 0.9%、1.6% 和 1.2%。实验表明,该算法能有效识别复杂场景中的苹果幼果,同时满足实时检测的要求,为苹果园的智能化管理提供了支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
YOUNG APPLE FRUITS DETECTION METHOD BASED ON IMPROVED YOLOV5
The intelligent detection of young apple fruits based on deep learning faced various challenges such as varying scale sizes and colors similar to the background, which increased the risk of misdetection or missed detection. To effectively address these issues, a method for young apple fruit detection based on improved YOLOv5 was proposed in this paper. Firstly, a young apple fruits dataset was established. Subsequently, a prediction layer was added to the detection head of the model, and four layers of CA attention mechanism were integrated into the detection neck (Neck). Additionally, the GIOU function was introduced as the model's loss function to enhance its overall detection performance. The accuracy on the validation dataset reached 94.6%, with an average precision of 82.2%. Compared with YOLOv3, YOLOv4, and the original YOLOv5 detection methods, the accuracy increased by 0.4%, 1.3%, and 4.6% respectively, while the average precision increased by 0.9%, 1.6%, and 1.2% respectively. The experiments demonstrated that the algorithm effectively recognized young apple fruits in complex scenes while meeting real-time detection requirements, providing support for intelligent apple orchard management.
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