Grape Maturity Detection and Visual Pre-Positioning Based on Improved YOLOv4

稳健性(进化) 计算机科学 人工智能 目标检测 模式识别(心理学) 卷积神经网络 卷积(计算机科学) 职位(财务) 人工神经网络 财务 生物化学 基因 经济 化学
作者
Chang Qiu,Guohang Tian,Jiawei Zhao,Qin Liu,Shangjie Xie,Kuicheng Zheng
出处
期刊:Electronics [MDPI AG]
卷期号:11 (17): 2677-2677 被引量:11
标识
DOI:10.3390/electronics11172677
摘要

To guide grape picking robots to recognize and classify the grapes with different maturity quickly and accurately in the complex environment of the orchard, and to obtain the spatial position information of the grape clusters, an algorithm of grape maturity detection and visual pre-positioning based on improved YOLOv4 is proposed in this study. The detection algorithm uses Mobilenetv3 as the backbone feature extraction network, uses deep separable convolution instead of ordinary convolution, and uses the h-swish function instead of the swish function to reduce the number of model parameters and improve the detection speed of the model. At the same time, the SENet attention mechanism is added to the model to improve the detection accuracy, and finally the SM-YOLOv4 algorithm based on improved YOLOv4 is constructed. The experimental results of maturity detection showed that the overall average accuracy of the trained SM-YOLOv4 target detection algorithm under the verification set reached 93.52%, and the average detection time was 10.82 ms. Obtaining the spatial position of grape clusters is a grape cluster pre-positioning method based on binocular stereo vision. In the pre-positioning experiment, the maximum error was 32 mm, the mean error was 27 mm, and the mean error ratio was 3.89%. Compared with YOLOv5, YOLOv4-Tiny, Faster_R-CNN, and other target detection algorithms, which have greater advantages in accuracy and speed, have good robustness and real-time performance in the actual orchard complex environment, and can simultaneously meet the requirements of grape fruit maturity recognition accuracy and detection speed, as well as the visual pre-positioning requirements of grape picking robots in the orchard complex environment. It can reliably indicate the growth stage of grapes, so as to complete the picking of grapes at the best time, and it can guide the robot to move to the picking position, which is a prerequisite for the precise picking of grapes in the complex environment of the orchard.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
1秒前
1秒前
离歌发布了新的文献求助10
2秒前
GuSiwen发布了新的文献求助10
2秒前
sunaijia发布了新的文献求助10
2秒前
2秒前
Lucas应助cg采纳,获得10
2秒前
3秒前
大大怪发布了新的文献求助10
3秒前
眼睛大白凝完成签到,获得积分10
3秒前
4秒前
5秒前
5秒前
充电宝应助李依林采纳,获得10
6秒前
橘x应助吴玉杰采纳,获得50
7秒前
赖林完成签到,获得积分10
7秒前
7秒前
华仔应助qweycl采纳,获得10
8秒前
科研通AI6.3应助111采纳,获得10
8秒前
kl完成签到,获得积分10
8秒前
YF是杨芳发布了新的文献求助10
10秒前
10秒前
赖林发布了新的文献求助10
10秒前
wang完成签到,获得积分10
10秒前
卓扬发布了新的文献求助10
10秒前
10秒前
11秒前
科研通AI2S应助徐小徐采纳,获得10
11秒前
NexusExplorer应助徐小徐采纳,获得10
12秒前
12秒前
鲤鱼雪曼发布了新的文献求助10
12秒前
12秒前
rin发布了新的文献求助30
13秒前
食分子发布了新的文献求助10
13秒前
14秒前
wxx完成签到,获得积分10
15秒前
冷艳寄真发布了新的文献求助10
15秒前
可爱的函函应助食分子采纳,获得10
16秒前
GuSiwen完成签到,获得积分10
16秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Modern Epidemiology, Fourth Edition 5000
Handbook of pharmaceutical excipients, Ninth edition 5000
Digital Twins of Advanced Materials Processing 2000
Weaponeering, Fourth Edition – Two Volume SET 2000
Social Cognition: Understanding People and Events 1000
Polymorphism and polytypism in crystals 1000
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 纳米技术 有机化学 物理 生物化学 化学工程 计算机科学 复合材料 内科学 催化作用 光电子学 物理化学 电极 冶金 遗传学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 6030211
求助须知:如何正确求助?哪些是违规求助? 7705005
关于积分的说明 16192383
捐赠科研通 5177165
什么是DOI,文献DOI怎么找? 2770477
邀请新用户注册赠送积分活动 1753894
关于科研通互助平台的介绍 1639389