Detection of surface defects for maize seeds based on YOLOv5

Abstract

The external appearance of maize seeds is one of important quality evaluation indicators for maize seeds. For traditional maize seed appearance quality detection, which mainly relies on naked eyes to inspect surface defects. Although computer vision as a relative mature way can be used for sample appearance quality inspection, manual feature extraction is still required. Meanwhile, machine learning technology, especially deep learning, has developed rapidly in the last decades. The maize seed surface defect detection coupled with deep learning can effectively replace traditional detection methods, reduce manual intervention, and decrease costs. In this paper, the collection and preprocessing of maize seed images, as well as the surface defects evaluation methods of maize seeds using a deep learning framework YOLOv5 were proposed. Firstly, in terms of image acquisition, maize seed batch surface defect detection system was established to obtain images. Then, the quality of maize seed images was improved by filtering, segmentation, and enhancement, which could significantly reduce noise in the images, separate the targets from the background and replace the background. Finally, ECA-Improved-YOLOv5S-Mobilenet model, which was established to improve the feature learning performance, could extracted the features from the maize seeds image and detect defects quickly at different levels. The experimental results showed that the precision was 92.8%, the recall rate was 98.9%, and the mPA0.5 was 95.5% with 8.8 MB of model size. In general, the proposed maize seeds surface defect detection method combined with deep learning could provide a theoretical support and technical basis for future development of seed grading and plantation.