Wang Houqiao, Guo Xiaoxue, Zhang Shihao, Li Gongming, Zhao Qiang, Wang Zejun
College of Tea Science, Yunnan Agricultural University, Kunming, China.
College of Mechanical and Electrical Engineering, Wuhan Donghu University, Wuhan, China.
PLoS One. 2025 Jan 8;20(1):e0312112. doi: 10.1371/journal.pone.0312112. eCollection 2025.
The quality and safety of tea food production is of paramount importance. In traditional processing techniques, there is a risk of small foreign objects being mixed into Pu-erh sun-dried green tea, which directly affects the quality and safety of the food. To rapidly detect and accurately identify these small foreign objects in Pu-erh sun-dried green tea, this study proposes an improved YOLOv8 network model for foreign object detection. The method employs an MPDIoU optimized loss function to enhance target detection performance, thereby increasing the model's precision in targeting. It incorporates the EfficientDet high-efficiency target detection network architecture module, which utilizes compound scale-centered anchor boxes and an adaptive feature pyramid to achieve efficient detection of targets of various sizes. The BiFormer bidirectional attention mechanism is introduced, allowing the model to consider both forward and backward dependencies in sequence data, significantly enhancing the model's understanding of the context of targets in images. The model is further integrated with sliced auxiliary super-inference technology and YOLOv8, which subdivides the image and conducts in-depth analysis of local features, significantly improving the model's recognition accuracy and robustness for small targets and multi-scale objects. Experimental results demonstrate that, compared to the original YOLOv8 model, the improved model has seen increases of 4.50% in Precision, 5.30% in Recall, 3.63% in mAP, and 4.9% in F1 score. When compared with the YOLOv7, YOLOv5, Faster-RCNN, and SSD network models, its accuracy has improved by 3.92%, 7.26%, 14.03%, and 11.30%, respectively. This research provides new technological means for the intelligent transformation of automated color sorters, foreign object detection equipment, and intelligent sorting systems in the high-quality production of Yunnan Pu-erh sun-dried green tea. It also provides strong technical support for the automation and intelligent development of the tea industry.
茶食品生产的质量与安全至关重要。在传统加工工艺中，存在小异物混入普洱茶晒青毛茶的风险，这直接影响食品的质量与安全。为快速检测并准确识别普洱茶晒青毛茶中的这些小异物，本研究提出一种用于异物检测的改进YOLOv8网络模型。该方法采用MPDIoU优化损失函数来提升目标检测性能，从而提高模型在目标定位上的精度。它融入了EfficientDet高效目标检测网络架构模块，该模块利用复合尺度中心锚框和自适应特征金字塔来实现对各种大小目标的高效检测。引入了BiFormer双向注意力机制，使模型能够考虑序列数据中的前向和后向依赖关系，显著增强模型对图像中目标上下文的理解。该模型还与切片辅助超推理技术和YOLOv8进一步集成，对图像进行细分并深入分析局部特征，显著提高了模型对小目标和多尺度物体的识别准确率和鲁棒性。实验结果表明，与原始YOLOv8模型相比，改进后的模型在精度上提高了4.50%，召回率提高了5.30%，平均精度均值提高了3.63%，F1分数提高了4.9%。与YOLOv7、YOLOv5、Faster-RCNN和SSD网络模型相比，其准确率分别提高了3.92%、7.26%、14.03%和11.30%。本研究为云南普洱茶晒青毛茶高质量生产中的自动色选机、异物检测设备和智能分拣系统的智能化改造提供了新的技术手段。也为茶产业的自动化和智能化发展提供了有力的技术支持。
