Wheat impurity content is one of the crucial indicators for evaluating wheat quality grades and prices, serving as an essential inspection parameter for grain storage. Due to the substantial intra-class variations in the aspect ratios of wheat impurities, traditional object detection algorithms fail to consider the diverse shapes and sizes of wheat impurities in real-world scenarios, resulting in low detection accuracy and missed detections. To address those issues, a YOLOv5-based wheat impurity automatic detection method, named Ro-YOLOv5, is proposed for computed tomography (CT) images of wheat samples. This method introduces rotated detection boxes and Circular Smooth Label (CSL) to transform angle regression predictions into angle classification predictions, effectively resolving issues related to angle periodicity and boundary exchange in regression; Additionally, the GIOU loss function in YOLOv5 is replaced with the CIOU loss function, which is more suitable for wheat impurity targets, ensuring the lower loss values and stable boundary regression. When creating the impurity-containing wheat dataset, a hybrid filtering method is introduced for leveraging the damping characteristics of the S-L filter for suppressing high-frequency components in projection images, thereby mitigating oscillation phenomena; Moreover, the Blackman filter is employed for image sharpening to preserve more image detail components. Experiments results have shown that the proposed algorithm achieves a mean average precision of 88.83% on the self-made dataset, representing a 7.56% improvement over the original YOLOv5 baseline network. The average detection time is 11.6ms, meeting the performance requirements for detecting wheat impurities.

中文翻译：

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Ro-YOLOv5：一种基于圆形平滑标签的小麦杂质检测仪


小麦杂质含量是评价小麦品质等级和价格的重要指标之一，是粮食储存的重要检验参数。由于小麦杂质的长宽比存在较大的类内差异，传统的目标检测算法无法考虑现实场景中小麦杂质的不同形状和大小，导致检测精度低和漏检。为了解决这些问题，提出了一种基于 YOLOv5 的小麦杂质自动检测方法，称为 Ro-YOLOv5，用于小麦样品的计算机断层扫描 (CT) 图像。该方法引入旋转检测框和圆形平滑标签（CSL），将角度回归预测转化为角度分类预测，有效解决回归中与角度周期性和边界交换相关的问题；此外，YOLOv5中的GIOU损失函数替换为CIOU损失函数，更适合小麦杂质目标，保证了较低的损失值和稳定的边界回归。在创建含杂质小麦数据集时，引入混合滤波方法，利用S-L滤波器的阻尼特性抑制投影图像中的高频成分，从而减轻振荡现象；此外，采用布莱克曼滤波器进行图像锐化，以保留更多的图像细节成分。实验结果表明，该算法在自制数据集上的平均精度达到88.83%，比原始YOLOv5基线网络提高了7.56%。平均检测时间为11.6ms，满足检测小麦杂质的性能要求。