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Tea Chrysanthemum Detection by Leveraging Generative Adversarial Networks and Edge Computing
- Source :
- Frontiers in Plant Science, Vol 13 (2022)
- Publication Year :
- 2022
- Publisher :
- Frontiers Media S.A., 2022.
-
Abstract
- A high resolution dataset is one of the prerequisites for tea chrysanthemum detection with deep learning algorithms. This is crucial for further developing a selective chrysanthemum harvesting robot. However, generating high resolution datasets of the tea chrysanthemum with complex unstructured environments is a challenge. In this context, we propose a novel tea chrysanthemum – generative adversarial network (TC-GAN) that attempts to deal with this challenge. First, we designed a non-linear mapping network for untangling the features of the underlying code. Then, a customized regularization method was used to provide fine-grained control over the image details. Finally, a gradient diversion design with multi-scale feature extraction capability was adopted to optimize the training process. The proposed TC-GAN was compared with 12 state-of-the-art generative adversarial networks, showing that an optimal average precision (AP) of 90.09% was achieved with the generated images (512 × 512) on the developed TC-YOLO object detection model under the NVIDIA Tesla P100 GPU environment. Moreover, the detection model was deployed into the embedded NVIDIA Jetson TX2 platform with 0.1 s inference time, and this edge computing device could be further developed into a perception system for selective chrysanthemum picking robots in the future.
Details
- Language :
- English
- ISSN :
- 1664462X
- Volume :
- 13
- Database :
- Directory of Open Access Journals
- Journal :
- Frontiers in Plant Science
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.17e4a7194e93409eb746be4becad212c
- Document Type :
- article
- Full Text :
- https://doi.org/10.3389/fpls.2022.850606