Real-Time Identification of Rice Weeds by UAV Low-Altitude Remote Sensing Based on Improved Semantic Segmentation Model

Real-time analysis of UAV low-altitude remote sensing images at airborne terminals facilitates the timely monitoring of weeds in the farmland. Aiming at the real-time identification of rice weeds by UAV low-altitude remote sensing, two improved identification models, MobileNetV2-UNet and FFB-BiSeNet...

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Autores principales:	Yubin Lan, Kanghua Huang, Chang Yang, Luocheng Lei, Jiahang Ye, Jianling Zhang, Wen Zeng, Yali Zhang, Jizhong Deng
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	low-altitude remote sensing semantic segmentation model real-time rice weeds target spraying Science Q
Acceso en línea:	https://doaj.org/article/3545b7338c78431a8b9dc465e394d63c
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spelling	oai:doaj.org-article:3545b7338c78431a8b9dc465e394d63c2021-11-11T18:54:52ZReal-Time Identification of Rice Weeds by UAV Low-Altitude Remote Sensing Based on Improved Semantic Segmentation Model10.3390/rs132143702072-4292https://doaj.org/article/3545b7338c78431a8b9dc465e394d63c2021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4370https://doaj.org/toc/2072-4292Real-time analysis of UAV low-altitude remote sensing images at airborne terminals facilitates the timely monitoring of weeds in the farmland. Aiming at the real-time identification of rice weeds by UAV low-altitude remote sensing, two improved identification models, MobileNetV2-UNet and FFB-BiSeNetV2, were proposed based on the semantic segmentation models U-Net and BiSeNetV2, respectively. The MobileNetV2-UNet model focuses on reducing the amount of calculation of the original model parameters, and the FFB-BiSeNetV2 model focuses on improving the segmentation accuracy of the original model. In this study, we first tested and compared the segmentation accuracy and operating efficiency of the models before and after the improvement on the computer platform, and then transplanted the improved models to the embedded hardware platform Jetson AGX Xavier, and used TensorRT to optimize the model structure to improve the inference speed. Finally, the real-time segmentation effect of the two improved models on rice weeds was further verified through the collected low-altitude remote sensing video data. The results show that on the computer platform, the MobileNetV2-UNet model reduced the amount of network parameters, model size, and floating point calculations by 89.12%, 86.16%, and 92.6%, and the inference speed also increased by 2.77 times, when compared with the U-Net model. The FFB-BiSeNetV2 model improved the segmentation accuracy compared with the BiSeNetV2 model and achieved the highest pixel accuracy and mean Intersection over Union ratio of 93.09% and 80.28%. On the embedded hardware platform, the optimized MobileNetV2-UNet model and FFB-BiSeNetV2 model inferred 45.05 FPS and 40.16 FPS for a single image under the weight accuracy of FP16, respectively, both meeting the performance requirements of real-time identification. The two methods proposed in this study realize the real-time identification of rice weeds under low-altitude remote sensing by UAV, which provide a reference for the subsequent integrated operation of plant protection drones in real-time rice weed identification and precision spraying.Yubin LanKanghua HuangChang YangLuocheng LeiJiahang YeJianling ZhangWen ZengYali ZhangJizhong DengMDPI AGarticlelow-altitude remote sensingsemantic segmentation modelreal-timerice weedstarget sprayingScienceQENRemote Sensing, Vol 13, Iss 4370, p 4370 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	low-altitude remote sensing semantic segmentation model real-time rice weeds target spraying Science Q
spellingShingle	low-altitude remote sensing semantic segmentation model real-time rice weeds target spraying Science Q Yubin Lan Kanghua Huang Chang Yang Luocheng Lei Jiahang Ye Jianling Zhang Wen Zeng Yali Zhang Jizhong Deng Real-Time Identification of Rice Weeds by UAV Low-Altitude Remote Sensing Based on Improved Semantic Segmentation Model
description	Real-time analysis of UAV low-altitude remote sensing images at airborne terminals facilitates the timely monitoring of weeds in the farmland. Aiming at the real-time identification of rice weeds by UAV low-altitude remote sensing, two improved identification models, MobileNetV2-UNet and FFB-BiSeNetV2, were proposed based on the semantic segmentation models U-Net and BiSeNetV2, respectively. The MobileNetV2-UNet model focuses on reducing the amount of calculation of the original model parameters, and the FFB-BiSeNetV2 model focuses on improving the segmentation accuracy of the original model. In this study, we first tested and compared the segmentation accuracy and operating efficiency of the models before and after the improvement on the computer platform, and then transplanted the improved models to the embedded hardware platform Jetson AGX Xavier, and used TensorRT to optimize the model structure to improve the inference speed. Finally, the real-time segmentation effect of the two improved models on rice weeds was further verified through the collected low-altitude remote sensing video data. The results show that on the computer platform, the MobileNetV2-UNet model reduced the amount of network parameters, model size, and floating point calculations by 89.12%, 86.16%, and 92.6%, and the inference speed also increased by 2.77 times, when compared with the U-Net model. The FFB-BiSeNetV2 model improved the segmentation accuracy compared with the BiSeNetV2 model and achieved the highest pixel accuracy and mean Intersection over Union ratio of 93.09% and 80.28%. On the embedded hardware platform, the optimized MobileNetV2-UNet model and FFB-BiSeNetV2 model inferred 45.05 FPS and 40.16 FPS for a single image under the weight accuracy of FP16, respectively, both meeting the performance requirements of real-time identification. The two methods proposed in this study realize the real-time identification of rice weeds under low-altitude remote sensing by UAV, which provide a reference for the subsequent integrated operation of plant protection drones in real-time rice weed identification and precision spraying.
format	article
author	Yubin Lan Kanghua Huang Chang Yang Luocheng Lei Jiahang Ye Jianling Zhang Wen Zeng Yali Zhang Jizhong Deng
author_facet	Yubin Lan Kanghua Huang Chang Yang Luocheng Lei Jiahang Ye Jianling Zhang Wen Zeng Yali Zhang Jizhong Deng
author_sort	Yubin Lan
title	Real-Time Identification of Rice Weeds by UAV Low-Altitude Remote Sensing Based on Improved Semantic Segmentation Model
title_short	Real-Time Identification of Rice Weeds by UAV Low-Altitude Remote Sensing Based on Improved Semantic Segmentation Model
title_full	Real-Time Identification of Rice Weeds by UAV Low-Altitude Remote Sensing Based on Improved Semantic Segmentation Model
title_fullStr	Real-Time Identification of Rice Weeds by UAV Low-Altitude Remote Sensing Based on Improved Semantic Segmentation Model
title_full_unstemmed	Real-Time Identification of Rice Weeds by UAV Low-Altitude Remote Sensing Based on Improved Semantic Segmentation Model
title_sort	real-time identification of rice weeds by uav low-altitude remote sensing based on improved semantic segmentation model
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3545b7338c78431a8b9dc465e394d63c
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Real-Time Identification of Rice Weeds by UAV Low-Altitude Remote Sensing Based on Improved Semantic Segmentation Model

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