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 生物化学 化学 基因 财务 经济
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
更新
大幅提高文件上传限制,最高150M (2024-4-1)

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
飘萍过客完成签到,获得积分10
1秒前
囡囡完成签到,获得积分10
2秒前
鸡毛完成签到,获得积分10
2秒前
orixero应助tooty采纳,获得10
3秒前
3秒前
念兹在兹发布了新的文献求助10
3秒前
大大大大管子完成签到 ,获得积分10
3秒前
3秒前
Hello应助祎思采纳,获得10
4秒前
华仔应助zunzun采纳,获得10
4秒前
影子发布了新的文献求助10
4秒前
iNk应助兴奋大开采纳,获得10
5秒前
6秒前
小帕菜完成签到,获得积分10
6秒前
花生完成签到 ,获得积分10
6秒前
6秒前
泡泡完成签到 ,获得积分10
7秒前
小马甲应助百汇科研采纳,获得10
8秒前
何果果完成签到,获得积分10
8秒前
老李完成签到,获得积分10
9秒前
9秒前
9秒前
科目三应助晚风采纳,获得50
10秒前
糕糕发布了新的文献求助10
11秒前
麦子完成签到 ,获得积分10
11秒前
She完成签到,获得积分10
11秒前
灵梦柠檬酸完成签到,获得积分10
11秒前
11秒前
沉默大白关注了科研通微信公众号
12秒前
12秒前
wufel完成签到,获得积分10
12秒前
liu完成签到 ,获得积分10
13秒前
驰驰发布了新的文献求助10
13秒前
yy完成签到 ,获得积分10
14秒前
jia完成签到 ,获得积分20
15秒前
Alrigh-t完成签到,获得积分10
16秒前
李小鑫吖发布了新的文献求助10
16秒前
可爱的函函应助入戏太深采纳,获得10
16秒前
CodeCraft应助竹喧私语采纳,获得10
17秒前
replica完成签到,获得积分10
17秒前
高分求助中
좌파는 어떻게 좌파가 됐나:한국 급진노동운동의 형성과 궤적 2500
Sustainability in Tides Chemistry 1500
TM 5-855-1(Fundamentals of protective design for conventional weapons) 1000
Cognitive linguistics critical concepts in linguistics 800
Threaded Harmony: A Sustainable Approach to Fashion 799
Livre et militantisme : La Cité éditeur 1958-1967 500
氟盐冷却高温堆非能动余热排出性能及安全分析研究 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3052912
求助须知:如何正确求助?哪些是违规求助? 2710137
关于积分的说明 7419790
捐赠科研通 2354754
什么是DOI,文献DOI怎么找? 1246249
科研通“疑难数据库(出版商)”最低求助积分说明 606002
版权声明 595975