Central Attention and a Dual Path Convolutional Neural Network in Real-World Tree Species Recognition

Yi Chung, Chih-Ang Chou and Chih-Yang Li
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Yi Chung: College of Human Development and Health, National Taipei University of Nursing and Health Sciences, Taipei 11219, Taiwan
Chih-Ang Chou: Xin Ji International Company, New Taipei 234014, Taiwan
Chih-Yang Li: Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan

IJERPH, 2021, vol. 18, issue 3, 1-27

Abstract: Identifying plants is not only the job of professionals, but also useful or essential for the plant lover and the general public. Although deep learning approaches for plant recognition are promising, driven by the success of convolutional neural networks (CNN), their performances are still far from the requirements of an in-field scenario. First, we propose a central attention concept that helps focus on the target instead of backgrounds in the image for tree species recognition. It could prevent model training from confused vision by establishing a dual path CNN deep learning framework, in which the central attention model combined with the CNN model based on InceptionV3 were employed to automatically extract the features. These two models were then learned together with a shared classification layer. Experimental results assessed the effectiveness of our proposed approach which outperformed each uni-path alone, and existing methods in the whole plant recognition system. Additionally, we created our own tree image database where each photo contained a wealth of information on the entire tree instead of an individual plant organ. Lastly, we developed a prototype system of an online/offline available tree species identification working on a consumer mobile platform that can identify the tree species not only by image recognition, but also detection and classification in real-time remotely.

Keywords: plant recognition; deep learning; dual path convolutional neural network; visual attention; mobile application (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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