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A Deep Learning-Based Object Detection Scheme by Improving YOLOv5 for Sprouted Potatoes Datasets

文献类型： 外文期刊

作者： Dai, Guowei 1 ; Hu, Lin 1 ; Fan, Jingchao 1 ; Yan, Shen 1 ; Li, Ruijing 1 ;

作者机构： 1.Agr Informat Inst CAAS, Natl Agr Sci Data Ctr, Beijing 100081, Peoples R China

2.Chinese Acad Agr Sci, Natl Nanfan Res Inst Sanya, Sanya 572024, Peoples R China

关键词： Object detection; convolutional neural network; sprouting potato recognition; mosaic; hyperparametric optimization; spatial pyramid pooling

期刊名称：IEEE ACCESS （ 影响因子：3.476； 五年影响因子：3.758 ）

ISSN： 2169-3536

年卷期： 2022 年 10 卷

页码：

收录情况： SCI

摘要： Detecting and eliminating sprouted potatoes is a basic measure before potato storage, which can effectively improve the quality of potatoes before storage and reduce economic losses due to potato spoilage and decay. In this paper, we propose an improved YOLOv5-based sprouted potato detection model for detecting and grading sprouted potatoes in complex scenarios. By replacing Cony with CrossConv in the C3 module, the feature similarity loss problem of the fusion process is improved, and the feature representation is enhanced. SPP is improved using fast spatial pyramid pooling to reduce feature fusion parameters and speed up feature fusion. The 9-Mosaic data augmentation algorithm improves the model generalization ability; the anchor points are reconstructed using the genetic algorithm k-means to enhance small target features, and then multi-scale training and hyperparameter evolution mechanisms are used to improve the accuracy. The experimental results show that the improved model has 90.14% recognition accuracy and 88.1% mAP, and the mAP is 4.6%, 7.5%, and 12.4% higher compared with SSD, YOLOv5, and YOLOv4, respectively. In summary, the improved YOLOv5 model, with good detection accuracy and effectiveness, can meet the requirements of rapid grading in automatic potato sorting lines.

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