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Liu, L, Wang, R, Xie, C, Yang, P, Sudirman, S, Wang, F and Li, R (2019) Deep Learning based Automatic Approach using Hybrid Global and Local Activated Features towards Large-scale Multi-class Pest Monitoring. In: 2019 IEEE 17th International Conference on Industrial Informatics (INDIN) . (IEEE International Conference on Industrial Informatics, 22 July 2019 - 25 July 2019, Helnsinki, Finland).
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Abstract
Monitoring pest in agriculture has been a high-priority issue all over the world. Computer vision techniques are widely utilized in practical crop pest prevention applications due to the rapid development of artificial intelligence technology. However, current deep learning image analytic approaches achieve low accuracy and poor robustness in agriculture pest monitoring task. This paper targets at this challenge by proposing a novel two-stage deep learning based automatic pest monitoring system with hybrid global and local activated feature. In this approach, a Global activated Feature Pyramid Network (GaFPN) is firstly proposed for extracting highly representative features of pests over both depth and spatial position activation levels. Then, an improved Local activated Region Proposal Network (LaRPN) augmenting contextual and attentional information is represented for precisely locating pest objects. Finally, we design a fully connected neural network to estimate the severity of input image under the detected pests. The experimental results on our 88.6K images dataset (with 16 types of common pests) show that our approach outweighs the state-of-the-art methods in industrial circumstances.
| Item Type: | Conference or Workshop Item (Paper) |
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| Additional Information: | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Computer Science & Mathematics |
| Date Deposited: | 07 Jun 2019 10:39 |
| Last Modified: | 31 May 2024 11:38 |
| DOI or ID number: | 10.1109/INDIN41052.2019.8972026 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/10849 |
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