|Table of Contents|

Apple Target Detection Method in Different Scenarios Based on YOLOv3

《北方园艺》[ISSN:1001-0009/CN:23-1247/S]

Issue:
2024年22
Page:
137-146
Research Field:
Publishing date:

Info

Title:
Apple Target Detection Method in Different Scenarios Based on YOLOv3
Author(s):
CHENG Zhenzhen1; CHENG Yifan2; MIAO Bailing1
(1.College of Horticulture，Xinyang Agriculture and Forestry College，Xinyang，Henan 464399；2.College of Optoelectronic Information Engineering，Huazhong University of Science and Technology，Wuhan，Hubei 430074)
Keywords:
apple; YOLOv3; target detection; deep learning
PACS:
S 431.9
DOI:
10.11937/bfyy.20241542
Abstract:
Apple-picking robots relied on precise apple target detection to achieve intelligent picking.However，conditions such as the number of apples，light intensity，and growth stages made apple detection complex.To address this，apples were used as test materials，and the YOLOv3 (You Only Look Once version 3) object detection method was employed to study the effectiveness of apple target detection in different scenarios.The anchor sizes of YOLOv3 were adjusted to optimize the variations in apple size according to the specific characteristics of apple shapes.To verify the effectiveness of the model，experiments were conducted using variables such as the number of apples，different lighting angles，and different growth stages of apples.The proposed algorithm was compared and analyzed with two existing algorithms，YOLOv2 (You Only Look Once version 2) and SSD (Single Shot MultiBox Detector)，in terms of target recognition effectiveness.The results indicated that under different conditions，the proposed method achieved F1 scores of over 88% in apple target detection，providing technical support for the development of apple-picking robots.

References:

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Last Update: 2024-12-03