1. Pest Identification Method Based on TPH-YOLOv5 Algorithm and Small Sample Learning
- Author
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ZHU Xiang-yuan, NIE Hong, ZHOU Xu
- Subjects
deep learning ,pest identification ,attention mechanism ,small sample learning ,tph-yolov5 ,data augmentation ,Computer software ,QA76.75-76.765 ,Technology (General) ,T1-995 - Abstract
Pest identification-based deep convolutional object detection is an important application of smart agriculture,which performs pest monitoring and ensures stable agricultural production.To solve the problems of high missed detection rate of small pests and low precision of small samples,a pest identification method based on the TPH-YOLOv5 algorithm and small sample learning is proposed.First,data augmentation for small objects and small samples is designed.Through copy and pasting,cropping,and oversampling,the number of training samples increases and the pest locations are diversified,which improves the contribution to training loss.Second,a two-stage small sample learning strategy based on fine-tuning is constructed.By learning the characteristics of basic and new categories of pests in different stages,the recognition precision of basic categories will not decrease while identifying new pests,which is suitable for the actual agricultural application of continuously collecting pest data.Finally,TPH-YOLOv5 is introduced as the pest identification algorithm.Rigorous tests are conducted on the 28 categories of pest images.The results show that the proposed method achieves high learning efficiency and recognition accuracy,with precision,recall,and mean average precision(mAP) of 87.6%,84.9% and 85.7%,respectively.
- Published
- 2022
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