Real-time detection of orchard cherry based on YOLOV4 model

doi:10.3969/j.issn.1004-1524.2022.11.21

Acta Agriculturae Zhejiangensis ›› 2022, Vol. 34 ›› Issue (11): 2522-2532.DOI: 10.3969/j.issn.1004-1524.2022.11.21

• Biosystems Engineering • Previous Articles Next Articles

Real-time detection of orchard cherry based on YOLOV4 model

ZHOU Pinzhi¹^,²(), PEI Yuekun¹^,²^,^*(), WEI Ran¹^,², ZHANG Yongfei¹^,², GU Yu¹^,²

1. Beidou High Precision Positioning Service Technology Engineering Laboratory of Liaoning Province, Dalian Unviersity, Dalian 116622, Liaoning, China
2. Environment Sensing and Intelligent Control Key Laboratory of Dalian,Dalian University, Dalian 116622, Liaoning, China

Received:2021-05-07 Online:2022-11-25 Published:2022-11-29
Contact: PEI Yuekun

Abstract

Abstract:

In order to solve the problem of difficulty in target recognition and low detection accuracy in the state monitoring of cherries in different growth periods under natural environment, this paper proposed an improved convolutional neural network cherry classification and detection model based on CSPDarknet53. The feature extraction network used in the classic YOLOV4 had a deeper number of layers and could extract more advanced abstract features, but had a weak local perception of the target. By integrating the CBAM attention mechanism on the CSPDarknet53 network structure, the perception of local features of the target was enhanced. Furthermore, the accuracy of target detection was improved, its feature extraction and target detection capabilities were better than the original algorithm, the feature layer output of the feature extraction network was adjusted, and the third layer output to the second layer output was changed to increase the acquisition of small target semantic information, k-means algorithm was used to optimize the size of the prior frame to adapt to the size of the cherry target, and ablation experiment analysis was conducted. The results showed that the improved YOLOV4 cherry detection model had an average accuracy of 92.31% and an F1 score of 87.3%, which was better than Faster RCNN, YOLOV3 and the original YOLOV4 algorithm. The detection speed was 40.23 frames·s^-1, which was suitable for natural environments. It provided a theoretical and technical basis for realizing automatic monitoring of fruit growth status in orchards.

Key words: cherry, maturity, attention mechanism

CLC Number:

S126

ZHOU Pinzhi, PEI Yuekun, WEI Ran, ZHANG Yongfei, GU Yu. Real-time detection of orchard cherry based on YOLOV4 model[J]. Acta Agriculturae Zhejiangensis, 2022, 34(11): 2522-2532.

Figures/Tables 15

References 16

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处理 Treatment	参数值Parameter
处理 Treatment	第一阶段 First stage	第二阶段 Second stage
Epoch	0~100	100~200
Batch size	6	6
Lr	1×10^-3	1×10^-4
Optimizer	Adam	Adam

处理 Treatment	参数值Parameter
处理 Treatment	第一阶段 First stage	第二阶段 Second stage
Epoch	0~100	100~200
Batch size	6	6
Lr	1×10^-3	1×10^-4
Optimizer	Adam	Adam

网络模型 Network model	AP/%			mAP/%	F1分数 F1-score/%
网络模型 Network model	未成熟果Immature	半成熟果Semi-mature	成熟果mature	mAP/%	F1分数 F1-score/%
YOLOV4	85.88	83.13	86.74	85.25	78.0
YOLOV4+mosaic	87.72	91.90	88.31	89.31	82.0
YOLOV4+CBAM	85.07	88.03	87.22	86.77	77.0
YOLOV4+k-means	85.90	84.36	86.98	85.75	78.6
本文方法Method of this article	90.92	91.98	94.04	92.31	87.3

网络模型 Network model	AP/%			mAP/%	F1分数 F1-score/%
网络模型 Network model	未成熟果Immature	半成熟果Semi-mature	成熟果mature	mAP/%	F1分数 F1-score/%
YOLOV4	85.88	83.13	86.74	85.25	78.0
YOLOV4+mosaic	87.72	91.90	88.31	89.31	82.0
YOLOV4+CBAM	85.07	88.03	87.22	86.77	77.0
YOLOV4+k-means	85.90	84.36	86.98	85.75	78.6
本文方法Method of this article	90.92	91.98	94.04	92.31	87.3

网络模型 Network model	FPS/ (幅·s^-1)	AP/%			精度 Precision/%	F1分数 F1-score/%
网络模型 Network model	FPS/ (幅·s^-1)	未成熟果 Immature	半成熟果 Semi-mature	成熟果 mature	精度 Precision/%	F1分数 F1-score/%
Faster RCNN	19.43	75.81	67.29	69.00	70.70	71.3
YOLOV3	52.13	85.79	75.15	87.92	82.95	78.6
YOLOV4	47.77	85.88	83.13	86.74	85.25	78.0
本文方法Method of this article	40.23	90.92	91.98	94.04	92.31	87.3

Real-time detection of orchard cherry based on YOLOV4 model

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