Cucumber automatic picking target detection method based on deep learning

• Main Authors: SU ZHANGSHUN, XIAO KEHUI
• Format: Patent
• Language: Chinese; English
• Published: 12.03.2024
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL; PHYSICS

The invention discloses a cucumber automatic picking target detection method based on deep learning, and the method comprises the steps: S1, constructing a cucumber fruit image data set, and dividing the data set into a training set, a verification set, and a test set; s2, preprocessing the image data set; step S3, constructing a YOLOv3 algorithm model; s4, a YOLOv3 algorithm model is trained, and parameter adjustment is carried out; and S5, detecting a cucumber fruit image by using the trained YOLOv3 algorithm model, and outputting a detection result, namely a fruit position. And a better detection effect is achieved by adjusting parameters of the YOLOv3 algorithm model. 本发明公开了基于深度学习的黄瓜自动采摘目标检测方法，包括：步骤S1，构建黄瓜果实图像数据集，将数据集划分为训练集、验证集和测试集；步骤S2，对图像数据集进行预处理；步骤S3，构建YOLOv3算法模型；步骤S4，训练YOLOv3算法模型，并进行参数的调整；步骤S5，利用训练后的YOLOv3算法模型检测黄瓜果实图像，输出检测结果，即果实位置。通过调整YOLOv3算法模型的参数，进而达到更好的检测效果。