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Zhang W, Liu Y, Chen K, Li H, Duan Y, Wu W, Shi Y, Guo W. Lightweight Fruit-Detection Algorithm for Edge Computing Applications. Front Plant Sci 2021; 12:740936. [PMID: 34721466 PMCID: PMC8548576 DOI: 10.3389/fpls.2021.740936] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 09/08/2021] [Indexed: 05/30/2023]
Abstract
In recent years, deep-learning-based fruit-detection technology has exhibited excellent performance in modern horticulture research. However, deploying deep learning algorithms in real-time field applications is still challenging, owing to the relatively low image processing capability of edge devices. Such limitations are becoming a new bottleneck and hindering the utilization of AI algorithms in modern horticulture. In this paper, we propose a lightweight fruit-detection algorithm, specifically designed for edge devices. The algorithm is based on Light-CSPNet as the backbone network, an improved feature-extraction module, a down-sampling method, and a feature-fusion module, and it ensures real-time detection on edge devices while maintaining the fruit-detection accuracy. The proposed algorithm was tested on three edge devices: NVIDIA Jetson Xavier NX, NVIDIA Jetson TX2, and NVIDIA Jetson NANO. The experimental results show that the average detection precision of the proposed algorithm for orange, tomato, and apple datasets are 0.93, 0.847, and 0.850, respectively. Deploying the algorithm, the detection speed of NVIDIA Jetson Xavier NX reaches 21.3, 24.8, and 22.2 FPS, while that of NVIDIA Jetson TX2 reaches 13.9, 14.1, and 14.5 FPS and that of NVIDIA Jetson NANO reaches 6.3, 5.0, and 8.5 FPS for the three datasets. Additionally, the proposed algorithm provides a component add/remove function to flexibly adjust the model structure, considering the trade-off between the detection accuracy and speed in practical usage.
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Affiliation(s)
- Wenli Zhang
- Department of Information, Beijing University of Technology, Beijing, China
| | - Yuxin Liu
- Department of Information, Beijing University of Technology, Beijing, China
| | - Kaizhen Chen
- Department of Information, Beijing University of Technology, Beijing, China
| | - Huibin Li
- Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
- Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture, Beijing, China
| | - Yulin Duan
- Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
- Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture, Beijing, China
| | - Wenbin Wu
- Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
- Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture, Beijing, China
| | - Yun Shi
- Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
- Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture, Beijing, China
| | - Wei Guo
- International Field Phenomics Research Laboratory, Institute for Sustainable Agro-Ecosystem Services, The University of Tokyo, Tokyo, Japan
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