Authors

Abstract

Accurate potato sprout detection is the key to automatic seed potato cutting, which is important for potato quality and yield. In this paper, a lightweight DAS-YOLOv8 model is proposed for the potato sprout detection task. By embedding DAS deformable attention in the feature extraction network and the feature fusion network, the global feature context can be efficiently represented and the attention increased to the relevant pixel image region; then, the C2f_Atten module fusing Shuffle attention is designed based on the C2f module to satisfy the attention to the key feature information of the high-level abstract semantics of the feature extraction network. At the same time, the ghost convolution is introduced to improve the C2f module and convolutional module to realize the decomposition of the redundant features to extract the key features. Verified on the collected potato sprout image data set, the average accuracy of the proposed DAS-YOLOv8 model is 94.25%, and the calculation amount is only 7.66 G. Compared with the YOLOv8n model, the accuracy is 2.13% higher, and the average accuracy is 1.55% higher. In comparison to advanced state-of-the-art (SOTA) target detection algorithms, the method in this paper offers a better balance between comprehensive performance and lightweight model design. The improved and optimized DAS-YOLOv8 model can realize the effective detection of potato sprouts, meet the requirements of real-time processing, and can provide theoretical support for the non-destructive detection of sprouts in automatic seed potato cutting.

Abstract in Chinese

马铃薯芽苗准确检测是马铃薯种薯自动切块的关键，对马铃薯的品质和产量具有重要意义。本文提出一种轻量级的DAS-YOLOv8模型用于马铃薯芽苗检测任务。通过在特征提取网络与特征融合网络嵌入DAS可变形注意力，以高效表示全局特征上下文和增加对相关像素图像区域的关注度；然后，基于C2f模块设计融合Shuffle注意力的C2f_Atten模块，以满足特征提取网络高层抽象语义关键特征信息的关注，同时引入幽灵卷积改进C2f模块和卷积模块，实现分解冗余特征提取关键特征。在采集到的马铃薯芽苗图像数据集进行验证，拟议DAS-YOLOv8模型的平均精度均值为94.25%，计算量仅为7.66 G，相比YOLOv8n模型，精准率提高2.13%，平均精度均值提高1.55%。在先进SOTA目标检测算法比较中，本文方法的综合性能更好模型更轻量化。改进优化后的DAS-YOLOv8模型能够实现马铃薯芽苗的有效检测，满足实时处理的要求，可为种薯自动切块中的芽苗无损检测提供理论支撑。