School of Medical Information Engineering, Zunyi Medical University, Zunyi, 563000, China.
School of Pharmacy, Zunyi Medical University, Zunyi, 563000, China.
Sci Rep. 2024 Jun 12;14(1):13505. doi: 10.1038/s41598-024-63398-w.
In recent years, with the increasing demand for high-quality Dendrobii caulis decoction piece, the identification of D. caulis decoction piece species has become an urgent issue. However, the current methods are primarily designed for professional quality control and supervision. Therefore, ordinary consumers should not rely on these methods to assess the quality of products when making purchases. This research proposes a deep learning network called improved YOLOv5 for detecting different types of D. caulis decoction piece from images. In the main architecture of improved YOLOv5, we have designed the C2S module to replace the C3 module in YOLOv5, thereby enhancing the network's feature extraction capability for dense and small targets. Additionally, we have introduced the Reparameterized Generalized Feature Pyramid Network (RepGFPN) module and Optimal Transport Assignment (OTA) operator to more effectively integrate the high-dimensional and low-dimensional features of the network. Furthermore, a new large-scale dataset of Dendrobium images has been established. Compared to other models with similar computational complexity, improved YOLOv5 achieves the highest detection accuracy, with an average mAP@.05 of 96.5%. It is computationally equivalent to YOLOv5 but surpasses YOLOv5 by 2 percentage points in terms of accuracy.
近年来，随着对高质量铁皮石斛饮片需求的增加，铁皮石斛饮片物种的鉴定已成为当务之急。然而，目前的方法主要是为专业的质量控制和监督设计的。因此，普通消费者在购买时不应该依靠这些方法来评估产品的质量。本研究提出了一种名为改进型 YOLOv5 的深度学习网络，用于从图像中检测不同类型的铁皮石斛饮片。在改进型 YOLOv5 的主要架构中，我们设计了 C2S 模块来替代 YOLOv5 中的 C3 模块，从而增强了网络对密集小目标的特征提取能力。此外，我们引入了 Reparameterized Generalized Feature Pyramid Network (RepGFPN) 模块和 Optimal Transport Assignment (OTA) 算子，以更有效地整合网络的高维和低维特征。此外，还建立了一个新的大型铁皮石斛图像数据集。与其他具有相似计算复杂度的模型相比，改进型 YOLOv5 实现了最高的检测精度，平均 mAP@.05 为 96.5%。它在计算上与 YOLOv5 相当，但在精度上比 YOLOv5 高出 2 个百分点。
