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PestNet: An End-to-End Deep Learning Approach for Large-Scale Multi-Class Pest Detection and Classification

Liu, L, Wang, R, Xie, C, Yang, P, Wang, F, Sudirman, S and Liu, W (2019) PestNet: An End-to-End Deep Learning Approach for Large-Scale Multi-Class Pest Detection and Classification. IEEE Access. ISSN 2169-3536

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Open Access URL: http://dx.doi.org/10.1109/ACCESS.2019.2909522 (Published version)


Multi-class pest detection is one of crucial components in pest management involving localization in addition to classification which is much more difficult than generic object detection because of the appearance differences among pest species. This paper proposes a region-based end-to-end approach named PestNet for large-scale multi-class pest detection and classification based on deep learning. PestNet consists of three major parts. Firstly, a novel module Channel-Spatial Attention (CSA) is proposed to be fused into Convolutional Neural Network (CNN) backbone for feature extraction and enhancement. The second one is called Region Proposal Network (RPN) that is adopted for providing region proposals as potential pest positions based on extracted feature maps from images. Position-Sensitive Score Map (PSSM), the third component, is used to replace Fully Connected (FC) layers for pest classification and bounding box regression. Furthermore, we apply Contextual RoI (Contextual Region of Interest) as contextual information of pest features to improve detection accuracy. We evaluate PestNet on our newly collected large-scale pests image dataset, Multi-class Pest Dataset 2018 (MPD2018) captured by our designed task-specific image acquisition equipment, covering more than 80k images with over 580k pests labeled by agricultural experts and categorized in 16 classes. Experimental results show that the proposed PestNet performs well on multi-class pest detection with 75.46% mean Average Precision (mAP), which outperforms the state-of-the-art methods.

Item Type: Article
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
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date Deposited: 03 Apr 2019 09:27
Last Modified: 03 Sep 2021 23:42
URI: https://researchonline.ljmu.ac.uk/id/eprint/10482
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