基于改进YOLOv8的番茄目标检测算法研究

doi:10.19788/j.issn.2096-6369.000075

Abstract

Abstract:

With the acceleration of the process of agricultural intelligence, the application of artificial intelligence technologies based on deep learning and robotics in agricultural production has attracted more and more attention. In order to solve the problems of high false recognition rate, low positioning accuracy and low picking efficiency of existing tomato fruit recognition methods in complex environments, an improved YOLOv8 network model was proposed to improve the detection accuracy and speed of automatic tomato fruit picking. The network takes YOLOv8 as the initial model, and adds the Deformable Convolution Module (DCN) to its backbone network, which effectively improves the detection accuracy of the model for small targets and reduces the missed detection rate. The SE attention mechanism module was introduced on the Neck side to improve the attention to the detection target. The Inner-IoU loss function is used to replace the original CIoU loss function to improve the regression accuracy of the bounding box in object detection. In this study, the average accuracy of the improved YOLOv8 model was increased by 7.2, 6.4, 6.6, and 7.7 percentage points compared with the SSD, YOLOv4, YOLOv5, and YOLOv7 network models, respectively, and the accuracy of the improved YOLOv8 model increased by 3.8%, the recall rate increased by 0.6%, and the mAP@0.5 and mAP@[0.5:0.95] increased by about 2.6% and 1.9%, respectively. The results show that the improved YOLOv8 model can effectively improve the accuracy and speed of automatic picking and detection of tomato fruits, which is of great significance for the realization of automatic picking of tomatoes.

Key words: tomato, YOLOv8, target recognition, deformable convolution network, attention mechanisms

WU Dan, MA XiaoJun, LIU DeSheng, SONG Wei, SU WenXian. Tomato Object Detection Algorithm Based on YOLOv8[J].Journal of Agricultural Big Data, 2025, 7(3): 281-293.

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配置	名称	具体信息
硬件环境	CPU	Intel （R）Core（TM）i7-11700
	GPU	英伟达 GTX 3080TI
	显存	12 GB
	内存	32 GB
软件环境	操作系统	Windows 11
	Python	Python-3.8.10
	Pytorch	torch-1.9.0+cu111
	CUDA	11.7

参数	设置	参数	设置
epochs	300	close_mosaic	10
patience	50	warmupepochs	3.0
batch	16	lrf	0.01
imgsz	224×224、416×416、640×640、800×800、960×960	lr0	0.01
workers	4	momentum	0.937
optimizer	SGD	weight_decay	0.0005

ratio	P(%)	R(%)	mAP@0.5(%)	mAP@[0.5:0.95](%)
0.5	55.9	44.3	45.4	27.9
0.7	56.1	45.6	45.2	27.6
1	57.7	44.2	46.1	28.3
1.25	56.4	45.3	45.8	28.5
1.5	56.9	45.7	46.0	28.1

模型 Method				P (%)	R (%)	mAP@0.5 (%)	mAP@[0.5:0.95] (%)
YOLOV8	DCN	SE	Inner-IOU	P (%)	R (%)	mAP@0.5 (%)	mAP@[0.5:0.95] (%)
√				57.7	60.1	58.5	45.3
√	√			59.2	61.7	59.5	45.9
		√		60.5	59.9	59.4	46.4
√			√	57.7	44.2	46.1	28.3
√	√	√		61.5	60.2	60.0	46.8
√	√	√	√	61.5	60.7	61.1	47.2

输入图像尺寸/(像素×像素)	GPU参数量	P精确率(%)	R召回率(%)	mAP@0.5(%)
224×224	5.80×10⁸	56.2	56.6	57.2
416×416	1.83×10⁹	57.3	59.6	58.1
640×640	4.52×10⁹	57.7	60.1	58.5
800×800	6.27×10⁹	57.7	60.5	58.9
960×960	9.47×10⁹	57.0	61.4	58.2

Tomato Object Detection Algorithm Based on YOLOv8

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模型名称	P精确率(%)	R召回率(%)	mAP@0.5(%)
SSD	51.3	56.4	53.9
Yolov4	53.6	55.2	54.7
Yolov5	53.5	55.9	54.5
Yolov7	51.9	53.5	53.4
Yolov8	57.7	60.1	58.5
Yolov8改进	61.5	60.7	61.1

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