Tea Chrysanthemum Detection by Leveraging Generative Adversarial Networks and Edge Computing

Qi, Chao, Gao, Junfeng, Chen, Kunjie , Shu, Lei and Pearson, Simon (2022) Tea Chrysanthemum Detection by Leveraging Generative Adversarial Networks and Edge Computing. Frontiers in plant science . ISSN 1664-462X

Full text not available from this repository.

Item Type:Article
Item Status:Live Archive

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 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 regularisation 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 0.1s inference time, and this edge computing device could be further developed into a perception system for selective chrysanthemum picking robots in the future.

Keywords:Tea chrysanthemum, generative adversarial network, deep learning, Edge computing, NVIDIA Jetson TX2
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
G Mathematical and Computer Sciences > G760 Machine Learning
G Mathematical and Computer Sciences > G740 Computer Vision
C Biological Sciences > C910 Applied Biological Sciences
Divisions:College of Science > Lincoln Institute for Agri-Food Technology
ID Code:48499
Deposited On:21 Mar 2022 10:22

Repository Staff Only: item control page