Foxtail Millet Ear Detection Approach Based on YOLOv4 and Adaptive Anchor Box Adjustment

Foxtail millet ear detection and counting are essential for the estimation of foxtail millet production and breeding. However, traditional foxtail millet ear counting approaches based on manual statistics are usually time-consuming and labor-intensive. In order to count the foxtail millet ears accur...

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Autores principales:	HAO Wangli, YU Peiyan, HAO Fei, HAN Meng, HAN Jiwan, SUN Weirong, LI Fuzhong
Formato:	article
Lenguaje:	EN ZH
Publicado:	Editorial Office of Smart Agriculture 2021
Materias:	foxtail millet ear detection yolov4 deep neural network dataset adaptive anchor box adjustment Agriculture (General) S1-972 Technology (General) T1-995
Acceso en línea:	https://doaj.org/article/c7f8b764de12465d9a6d129690a6c6de
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spelling	oai:doaj.org-article:c7f8b764de12465d9a6d129690a6c6de2021-11-17T07:52:00ZFoxtail Millet Ear Detection Approach Based on YOLOv4 and Adaptive Anchor Box Adjustment2096-809410.12133/j.smartag.2021.3.1.202102-SA066https://doaj.org/article/c7f8b764de12465d9a6d129690a6c6de2021-03-01T00:00:00Zhttp://www.smartag.net.cn/article/2021/2096-8094/2096-8094-2021-3-1-63.shtmlhttps://doaj.org/toc/2096-8094Foxtail millet ear detection and counting are essential for the estimation of foxtail millet production and breeding. However, traditional foxtail millet ear counting approaches based on manual statistics are usually time-consuming and labor-intensive. In order to count the foxtail millet ears accurately and efficiently, an adaptive anchor box adjustment foxtail millet ear detection method was proposed in this research. Ear detection dataset was firstly established, including 784 images and 10,000 ear samples. Furthermore, a novel foxtail millet ear detection approach based on YOLOv4 (You Only Look Once) was developed to quickly and accurately detect the ear of foxtail millet in the specific box. For verifying the effectiveness of the proposed approach, several criteria, including the mean average Precision, F1-score, Recall and mAP were employed. Moreover, ablation studies were designed to validate the effectiveness of the proposed method, including (1) evaluating the performance of the proposed model through comparing with other models (YOLOv2, YOLOv3 and Faster-RCNN); (2) evaluating the model with different Intersection over Union (IOU) thresholds to achieve the optimal IOU thresholds; (3) evaluating the foxtail millet ear detection with or without anchor boxes adjustment to verify the effectiveness of the adjustment of anchor boxes;(4) evaluating the changing reasons of model criteria and (5) evaluating the foxtail millet ear detection with different input original image size respectively. Experimental results showed that YOLOv4 could obtain the superior ear detection performance. Specifically, mAP and F1-score of YOLOv4 achieved 78.99% and 83.00%, respectively. The Precision was 87% and the Recall was 79.00%, which was about 8% better than YOLOv2, YOLOv3 and Faster RCNN models, in terms of all criteria. Moreover, experimental results indicates that the proposed method is superior with promising accuracy and faster speed.HAO WangliYU PeiyanHAO FeiHAN MengHAN JiwanSUN WeirongLI FuzhongEditorial Office of Smart Agriculturearticlefoxtail millet ear detectionyolov4deep neural networkdatasetadaptive anchor box adjustmentAgriculture (General)S1-972Technology (General)T1-995ENZH智慧农业, Vol 3, Iss 1, Pp 63-74 (2021)
institution	DOAJ
collection	DOAJ
language	EN ZH
topic	foxtail millet ear detection yolov4 deep neural network dataset adaptive anchor box adjustment Agriculture (General) S1-972 Technology (General) T1-995
spellingShingle	foxtail millet ear detection yolov4 deep neural network dataset adaptive anchor box adjustment Agriculture (General) S1-972 Technology (General) T1-995 HAO Wangli YU Peiyan HAO Fei HAN Meng HAN Jiwan SUN Weirong LI Fuzhong Foxtail Millet Ear Detection Approach Based on YOLOv4 and Adaptive Anchor Box Adjustment
description	Foxtail millet ear detection and counting are essential for the estimation of foxtail millet production and breeding. However, traditional foxtail millet ear counting approaches based on manual statistics are usually time-consuming and labor-intensive. In order to count the foxtail millet ears accurately and efficiently, an adaptive anchor box adjustment foxtail millet ear detection method was proposed in this research. Ear detection dataset was firstly established, including 784 images and 10,000 ear samples. Furthermore, a novel foxtail millet ear detection approach based on YOLOv4 (You Only Look Once) was developed to quickly and accurately detect the ear of foxtail millet in the specific box. For verifying the effectiveness of the proposed approach, several criteria, including the mean average Precision, F1-score, Recall and mAP were employed. Moreover, ablation studies were designed to validate the effectiveness of the proposed method, including (1) evaluating the performance of the proposed model through comparing with other models (YOLOv2, YOLOv3 and Faster-RCNN); (2) evaluating the model with different Intersection over Union (IOU) thresholds to achieve the optimal IOU thresholds; (3) evaluating the foxtail millet ear detection with or without anchor boxes adjustment to verify the effectiveness of the adjustment of anchor boxes;(4) evaluating the changing reasons of model criteria and (5) evaluating the foxtail millet ear detection with different input original image size respectively. Experimental results showed that YOLOv4 could obtain the superior ear detection performance. Specifically, mAP and F1-score of YOLOv4 achieved 78.99% and 83.00%, respectively. The Precision was 87% and the Recall was 79.00%, which was about 8% better than YOLOv2, YOLOv3 and Faster RCNN models, in terms of all criteria. Moreover, experimental results indicates that the proposed method is superior with promising accuracy and faster speed.
format	article
author	HAO Wangli YU Peiyan HAO Fei HAN Meng HAN Jiwan SUN Weirong LI Fuzhong
author_facet	HAO Wangli YU Peiyan HAO Fei HAN Meng HAN Jiwan SUN Weirong LI Fuzhong
author_sort	HAO Wangli
title	Foxtail Millet Ear Detection Approach Based on YOLOv4 and Adaptive Anchor Box Adjustment
title_short	Foxtail Millet Ear Detection Approach Based on YOLOv4 and Adaptive Anchor Box Adjustment
title_full	Foxtail Millet Ear Detection Approach Based on YOLOv4 and Adaptive Anchor Box Adjustment
title_fullStr	Foxtail Millet Ear Detection Approach Based on YOLOv4 and Adaptive Anchor Box Adjustment
title_full_unstemmed	Foxtail Millet Ear Detection Approach Based on YOLOv4 and Adaptive Anchor Box Adjustment
title_sort	foxtail millet ear detection approach based on yolov4 and adaptive anchor box adjustment
publisher	Editorial Office of Smart Agriculture
publishDate	2021
url	https://doaj.org/article/c7f8b764de12465d9a6d129690a6c6de
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Foxtail Millet Ear Detection Approach Based on YOLOv4 and Adaptive Anchor Box Adjustment

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