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- Title
Fast Self-Attention Deep Detection Network Based on Weakly Differentiated Plant Nematodess.
- Authors
Zhuang, Jiayan; Liu, Yangming; Xu, Ningyuan; Zhu, Yi; Xiao, Jiangjian; Gu, Jianfeng; Mao, Tianyi
- Abstract
High-precision, high-speed detection and classification of weakly differentiated targets has always been a difficult problem in the field of image vision. In this paper, the detection of phytopathogenic Bursaphelenchus xylophilus with small size and very weak inter-species differences is taken as an example. Our work is aimed at the current problem of weakly differentiated target detection: We propose a lightweight self attention network. Experiments show that the key feature recognition areas of plant nematodes found by our Self Attention network are in good agreement with the experience and knowledge of customs experts, and the feature areas found by this method can obtain higher detection accuracy than expert knowledge; In order to optimize the computing power brought by the whole image input, we use low resolution images to quickly obtain the location coordinates of key features, and then obtain the information of high resolution feature regions based on the coordinates; The adaptive weighted multi feature joint detection method based on heat map brightness is adopted to further improve the detection accuracy; We have constructed a more complete high-resolution training data set, involving 24 species of Equisetum and other common hybrids, with a total data volume of more than 10,000. The algorithm proposed in this paper replaces the tedious extensive manual labelling in the training process, improves the average training time of the model by more than 50%, reduces the testing time of a single sample by about 27%, optimizes the model storage size by 65%, improves the detection accuracy of the ImageNet pre-trained model by 12.6%, and improves the detection accuracy of the no-ImageNet pre-trained model by more than 48%.
- Subjects
PLANT nematodes; NEMATODES; NEMATODE-plant relationships; VISUAL fields; TRAINING manuals
- Publication
Electronics (2079-9292), 2022, Vol 11, Issue 21, p3497
- ISSN
2079-9292
- Publication type
Article
- DOI
10.3390/electronics11213497