基于少量标注样本的茶芽目标检测YSVD-Tea算法

YSVD-Tea Algorithm for Tea Bud Object Detection Based on Few Annotated Samples

构建大规模茶芽目标检测数据集是一项耗时且繁琐的任务,为了降低数据集构建成本,探索少量标注样本的算法尤为必要。本文提出了YSVD-Tea(YOLO singular value decomposition for tea bud detection)算法,通过将预训练模型中的基础卷积替换为3个连续的矩阵结构,实现了对YOLOX算法结构的重构。通过维度变化和奇异值分解操作,将预训练权重转换为与重构算法结构相对应的权重,从而将需要进行迁移学习的权重和需要保留的权重分离开,实现保留预训练模型先验信息的目的。在3种不同数量的数据集上分别进行了训练和验证。在最小数量的1/3数据集上,YSVD-Tea算法相较于改进前的YOLOX算法,mAP提高20.3个百分点。对比测试集与训练集的性能指标,YSVD-Tea算法在测试集与训练集的mAP差距仅为21.9%,明显小于YOLOX的40.6%和Faster R-CNN的55.4%。在数量最大的数据集上,YOLOX算法精确率、召回率、F1值、mAP分别为86.4%、87.0%、86.7%和88.3%,相较于对比算法均最高。YSVD-Tea在保证良好性能的同时,能够更好地适应少量标注样本的茶芽目标检测任务。

Constructing a large-scale dataset for tea bud object detection is a time-consuming and intricate task.To mitigate the cost of dataset construction,exploring algorithms with a minimal number of annotated samples is particularly necessary.The YOLO singular value decomposition for tea bud detection(YSVD-Tea)algorithm was introduced,which achieved the reconstruction of the YOLOX structure by replacing the basic convolution in the pre-trained model with three consecutive matrix structures.Through dimension transformation and singular value decomposition operations,pre-trained weights were converted into weights corresponding to the reconstructed algorithm structure,thereby separating the weights that require transfer learning from those that needed to be retained.This achieved the goal of preserving the general semantic information of the pre-trained model.Training and validation on three datasets of varying sizes were conducted.On the smallest 1/3 dataset,the YSVD-Tea algorithm showed a 20.3 percentage points improvement in mAP compared with the original YOLOX algorithm.Comparing performance metrics between the test and training sets,the mAP difference for the YSVD-Tea algorithm was only 21.9%,which was significantly lower than YOLOX's 40.6%and Faster R-CNN's 55.4%.In training with the largest complete dataset,the YOLOX algorithm achieved precision,recall,F1 score,and mAP of 86.4%,87.0%,86.7%,and 88.3%,respectively,surpassing the comparison algorithms.YSVD-Tea algorithm demonstrated superior suitability for the task of tea bud object detection,especially when confronted with a limited number of annotated samples.