Treffer: 基于改进 YOLOv7 的核桃仁分级研究与试验.

In China, walnut kernel grading predominantly relies on mechanical and manual methods, which suffer from high economic costs, low efficiency, and poor accuracy, severely limiting the standardization and economic value of walnut kernel products. To achieve automated and intelligent walnut kernel grading, this study proposes a deep learning-based intelligent grading method. An image acquisition device was utilized to capture walnut kernel images across six grades under three distinct background colors: pure white, light green, and conveyor belt. The Python OpenCV library was employed for data augmentation to expand the dataset, resulting in a VOC format dataset of 12 246 images, with training, testing, and validation sets allocated in an 8: 1: 1 ratio. To select the most suitable model for walnut kernel grading, two models were constructed based on the YOLOv5 and YOLOv7 object detection networks, respectively. Both models were trained using pre-trained weights, achieving average grading accuracies of 87. 83% and 91. 16% for YOLOv5 and YOLOv7, respectively. During validation, YOLOv7 exhibited mispredictions, prompting the integration of two attention mechanisms, ECANet and CBAM, to refine the model. The improved models demonstrated enhanced performance, with the YOLOv7+CBAM model achieving the highest average accuracy (94. 5%) and F1-score (90. 2%) . Compared to the original YOLOv7, the upgraded model increased average accuracy and F1-score by 3. 34% and 5. 9%, respectively, while adding only 2ms to inference time. To validate practical feasibility, a recognition system platform was developed for walnut kernel classification testing. Four experimental groups were designed, each containing 120 walnut kernels (20 per grade) . The YOLOv7+CBAM model achieved an average recognition accuracy of 91. 63%, demonstrating robust grading capabilities for walnut kernel appearance quality. This study provides a reference for intelligent walnut kernel grading systems. [ABSTRACT FROM AUTHOR]

中国核桃仁分级大多依靠机械和人工, 经济成本高、效率低、精度差, 严重影响核桃仁产品的标准化和经济价值。 为 实现核桃仁分级的自动化与智能化, 本研究提出基于深度学习的核桃仁智能分级方法。 利用图像采集装置采集纯白、淡 绿与传送带 3 种不同颜色背景下的 6 个等级的核桃仁图像, 使用 Python 的 OpenCV 库对图像进行数据增强扩充数据集, 建 立 VOC 格式数据集 12 246 张, 其中训练集、测试集与验证集的比例设置为 8: 1: 1。为选取更适合完成核桃仁分级的模型, 分别基于目标检测网络 YOLOv5 和 YOLOv7 构建核桃仁分级模型, 采用加载预训练权重的方式训练模型, 得到 YOLOv5 和 YOLOv7 模型核桃仁分级的平均准确率分别为 87. 83% 和 91. 16%。在验证过程中 YOLOv7 预测到了错误的对象, 引 入两种注意力机制 ECANet 与 CBAM 对 YOLOv7 进行改进, 改进后的两种模型的训练效果均有所提升, 其中 YOLOv7+ CBAM 模型效果更好, 平均准确率为 94. 5% 、F1 评分为 90. 2%。改进后的 YOLOv7 核桃仁分类平均准确率和 F1 评分比 YOLOv7 高出 3. 34% 和 5. 9%, 并且推理时间比 YOLOv7 仅增加 2 ms。 为验证模型的可行性, 搭建识别系统平台进行核 桃仁等级识别分类测试, 设置 6 种等级的核桃仁各 20 个, 共 120 个核桃仁为一组, 共 4 组试验。 得到 YOLOv7+CBAM 模 型的核桃仁平均识别正确率为 91. 63%。改进后的 YOLOv7+CBAM 模型可以实现对核桃仁外观品质的良好分级。 本 研究可为核桃仁智能化分级提供参考。 [ABSTRACT FROM AUTHOR]

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