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Deep Learning based Automatic Multi-Class Wild Pest Monitoring Approach using Hybrid Global and Local Activated Features with Stationary Trap Devices

Sudirman, S, Liu, L, Wang, R, Xie, C, Yang, P, Wang, F and Liu, W Deep Learning based Automatic Multi-Class Wild Pest Monitoring Approach using Hybrid Global and Local Activated Features with Stationary Trap Devices. IEEE Transactions on Industrial Informatics. ISSN 1551-3203 (Accepted)

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Abstract

Specialized control of pests and diseases have been a high-priority issue for agriculture industry in many countries. On account of automation and cost-effectiveness, image analytic based pest recognition systems are widely utilized in practical crops prevention applications. But due to powerless handcrafted features, current image analytic approaches achieve low accuracy and poor robustness in practical large-scale multi-class pest detection and recognition. To tackle this problem, this paper proposes a novel deep learning based automatic approach using hybrid and local activated features for pest monitoring solution. In the presented method, we exploit the global information from feature maps to build our Global activated Feature Pyramid Network (GaFPN) to extract pests highly discriminative features across various scales over both depth and position levels. It makes changes of depth or spatial sensitive features in pest images more visible during downsampling. Next, an improved pest localization module named Local activated Region Proposal Network (LaRPN) is proposed to find the precise pest objects positions by augmenting contextualized and attentional information for feature completion and enhancement in local level. The approach is evaluated on our 7-year large-scale pest dataset containing 88.6K images (16 types of pests) with 582.1K manually labelled pest objects. The experimental results show that our solution performs over 74.24% mAP in industrial circumstances, which outweighs two other state-of-the-art methods: Faster R-CNN [12] with mAP up to 70% and FPN [13] mAP up to 72%. Our code and dataset will be made publicly available.

Item Type: Article
Additional Information: © 2020 IEEE.
Uncontrolled Keywords: 08 Information and Computing Sciences, 09 Engineering, 10 Technology
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Computer Science & Mathematics
Publisher: IEEE
Date Deposited: 05 May 2020 08:57
Last Modified: 05 May 2020 11:15
URI: https://researchonline.ljmu.ac.uk/id/eprint/12876

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