Recognition and Location of Pepper Picking Based on Improved YOLOv5s and Depth Camera

IF 0.8 4区 农林科学 Q4 AGRICULTURAL ENGINEERING Applied Engineering in Agriculture Pub Date : 2023-01-01 DOI:10.13031/aea.15347
Sixing Liu, Ming Liu, Yan Chai, Shuang Li, H. Miao
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Abstract

HighlightsAn improved YOLOv5s deep learning model was used to identify peppers in complex background.The deep-level features on 3D (O-XYZ) coordinate of peppers were extracted using RealSense depth camera.An image database set of pepper in different scenes was established.A pepper recognition and location system were constructed based on improved YOLOv5s network.The proposed method achieved a mean average precision of 95.6% and minimum depth error of 0.001 m.Abstract. In order to investigate the impact of different scenes on the recognition performance and obtain the location information of picking targets, the recognition and location system based on improved YOLOv5s network and RealSense depth camera was constructed in this study. An image database in different scenes was established including light intensity, occlusion and overlap degree of pepper. An improved YOLOv5s deep learning model with bidirectional feature pyramid network (BiFPN) was used for the deep feature extraction and high-precision detection of pepper, and the effects of different scenes on recognition accuracy of the model were studied. The results showed that mean average precision (mAP) of YOLOv5s model reached 0.956, which was respectively 6.1%, 9.3%, 44.4%, and 8.2% higher than that of YOLOv4, YOLOv3, YOLOv2, and Faster R-CNN model. The model had good robustness under daytime and evening scenes with the mAP value higher than 0.9. The detection accuracy of the model in the leaf occlusion scenes was better than that of fruit overlap. The detection error was 0.001m which could not affect the picking positioning precision when the Z value of three-dimensional coordinates (O-XYZ) of pepper was 0.2 m. The improved algorithm can accurately recognize and extract three-dimensional coordinates of pepper, which reduces the calculations by eliminating lots of duplicate and redundant prediction boxes and provides a reference for trajectory planning of pepper picking operation. Keywords: Different scenes, Pepper recognition and location, Picking operation, YOLOv5s.
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基于改进YOLOv5s和深度相机的辣椒采摘识别与定位
HighlightsAn改进的yolov5深度学习模型用于复杂背景下辣椒的识别。利用RealSense深度相机提取辣椒三维(O-XYZ)坐标上的深层特征。建立了不同场景下辣椒的图像数据库集。基于改进的YOLOv5s网络构建了辣椒识别定位系统。该方法的平均精度为95.6%,最小深度误差为0.001 m。为了研究不同场景对识别性能的影响,获取拾取目标的位置信息,本研究构建了基于改进的YOLOv5s网络和RealSense深度相机的识别定位系统。建立了包括光照强度、遮挡度和辣椒重叠度在内的不同场景图像数据库。采用改进的YOLOv5s双向特征金字塔网络(BiFPN)深度学习模型对辣椒进行深度特征提取和高精度检测,研究了不同场景对模型识别精度的影响。结果表明,YOLOv5s模型的平均精度(mAP)达到0.956,比YOLOv4、YOLOv3、YOLOv2和Faster R-CNN模型分别提高6.1%、9.3%、44.4%和8.2%。该模型在白天和夜景下具有较好的鲁棒性,mAP值均大于0.9。该模型在树叶遮挡场景下的检测精度优于水果重叠场景。当辣椒三维坐标(O-XYZ) Z值为0.2 m时,检测误差为0.001m,不影响采摘定位精度。改进后的算法能够准确地识别和提取辣椒的三维坐标,消除了大量重复和冗余的预测框,减少了计算量,为辣椒采摘作业的轨迹规划提供了参考。关键词:不同场景,辣椒识别与定位,采摘操作,YOLOv5s
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来源期刊
Applied Engineering in Agriculture
Applied Engineering in Agriculture 农林科学-农业工程
CiteScore
1.80
自引率
11.10%
发文量
69
审稿时长
6 months
期刊介绍: This peer-reviewed journal publishes applications of engineering and technology research that address agricultural, food, and biological systems problems. Submissions must include results of practical experiences, tests, or trials presented in a manner and style that will allow easy adaptation by others; results of reviews or studies of installations or applications with substantially new or significant information not readily available in other refereed publications; or a description of successful methods of techniques of education, outreach, or technology transfer.
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