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基于深度学习框架和物联网的远程昆虫诱捕监测系统。

Remote Insects Trap Monitoring System Using Deep Learning Framework and IoT.

机构信息

Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore.

Department of Electrical Engineering, University of Western Ontario, London, ON N6A 3K7, Canada.

出版信息

Sensors (Basel). 2020 Sep 15;20(18):5280. doi: 10.3390/s20185280.

DOI:10.3390/s20185280
PMID:32942750
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7571233/
Abstract

Insect detection and control at an early stage are essential to the built environment (human-made physical spaces such as homes, hotels, camps, hospitals, parks, pavement, food industries, etc.) and agriculture fields. Currently, such insect control measures are manual, tedious, unsafe, and time-consuming labor dependent tasks. With the recent advancements in Artificial Intelligence (AI) and the Internet of things (IoT), several maintenance tasks can be automated, which significantly improves productivity and safety. This work proposes a real-time remote insect trap monitoring system and insect detection method using IoT and Deep Learning (DL) frameworks. The remote trap monitoring system framework is constructed using IoT and the Faster RCNN (Region-based Convolutional Neural Networks) Residual neural Networks 50 (ResNet50) unified object detection framework. The Faster RCNN ResNet 50 object detection framework was trained with built environment insects and farm field insect images and deployed in IoT. The proposed system was tested in real-time using four-layer IoT with built environment insects image captured through sticky trap sheets. Further, farm field insects were tested through a separate insect image database. The experimental results proved that the proposed system could automatically identify the built environment insects and farm field insects with an average of 94% accuracy.

摘要

在早期阶段对昆虫进行检测和控制对于建筑环境（人为的物理空间，如家庭、酒店、营地、医院、公园、人行道、食品工业等）和农业领域至关重要。目前，这些昆虫控制措施依赖于人工、繁琐、不安全且耗时的劳动。随着人工智能 (AI) 和物联网 (IoT) 的最新进展，许多维护任务可以实现自动化，从而显著提高生产力和安全性。本工作提出了一种使用物联网和深度学习 (DL) 框架的实时远程昆虫诱捕监测系统和昆虫检测方法。远程诱捕监测系统框架使用物联网和基于区域的卷积神经网络 (Faster RCNN) 残差神经网络 50 (ResNet50) 统一目标检测框架构建。使用建筑环境昆虫和农田昆虫图像对 Faster RCNN ResNet 50 目标检测框架进行训练，并在物联网中进行部署。使用通过粘性诱捕片捕获的建筑环境昆虫图像的四层物联网实时测试了所提出的系统。此外，还通过单独的昆虫图像数据库测试了农田昆虫。实验结果证明，所提出的系统可以自动识别建筑环境昆虫和农田昆虫，平均准确率为 94%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e5c/7571233/031f9888801b/sensors-20-05280-g010.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e5c/7571233/6f43b6f5e746/sensors-20-05280-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e5c/7571233/eec34b462e1f/sensors-20-05280-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e5c/7571233/29b2217a67b7/sensors-20-05280-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e5c/7571233/525c9eb2e5eb/sensors-20-05280-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e5c/7571233/031f9888801b/sensors-20-05280-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e5c/7571233/518125cd582b/sensors-20-05280-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e5c/7571233/3c25ec55fbc3/sensors-20-05280-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e5c/7571233/91d16d6f256b/sensors-20-05280-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e5c/7571233/3066a52972bc/sensors-20-05280-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e5c/7571233/8ba2d1f719c5/sensors-20-05280-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e5c/7571233/6f43b6f5e746/sensors-20-05280-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e5c/7571233/eec34b462e1f/sensors-20-05280-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e5c/7571233/29b2217a67b7/sensors-20-05280-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e5c/7571233/525c9eb2e5eb/sensors-20-05280-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e5c/7571233/031f9888801b/sensors-20-05280-g010.jpg

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