采用改进YOLOv3算法检测青皮核桃  

Detection of green walnut by improved YOLOv3

作　　者：郝建军[1,2] 邴振凯 杨淑华 杨杰[3] 孙磊 Hao Jianjun;Bing Zhenkai;Yang Shuhua;Yang Jie;Sun Lei(College of Mechanical and Electrical Engineering,Hebei Agricultural University,Baoding 071000,China;Technology Innovation Center of Intelligent Agricultural Equipment of Hebei Province,Baoding 071001,China;Huanghua Branch of Beijing Computing Center Co.,Ltd.,Cangzhou 061000,China)

机构地区：[1]河北农业大学机电工程学院,保定071000 [2]河北省智慧农业装备技术创新中心,保定071001 [3]北京市计算中心有限公司黄骅分中心,沧州061000

出　　处：《农业工程学报》2022年第14期183-190,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：河北省重点研发计划项目(21327211D);2022年河北省博士在读研究生创新能力培养资助项目(CXZZBS2022050)。

摘　　要：使用机器视觉对果实检测并进行估产是实现果园智能化管理的重要途径,针对自然环境下青皮核桃与叶片颜色差异小、核桃体积较小导致青皮核桃不易检出的问题,该研究提出一种基于改进YOLOv3的青皮核桃视觉检测方法。依据数据集特征进行数据增强,引入Mixup数据增强方法,使模型从更深的维度学习核桃特征;针对核桃单种类目标检测比较不同预训练模型,选择精度提升更明显的Microsoft Common Objects in Context(COCO)数据集预训练模型;依据标注框尺寸统计对锚框进行调整,避免锚框集中,提升模型多尺度优势。在消融试验中,前期改进将平均精度均值提升至93.30%,在此基础上,引入Mobil Net-v3骨干网络替换YOLOv3算法中原始骨干网络,提升模型检测能力及轻量化。试验表明,基于改进YOLOv3的青皮核桃检测平均精度均值为94.52%,超越YOLOv3其他2个骨干网络和Faster RCNN-ResNet-50网络。改进模型大小为88.6 M,检测速度为31帧/s,检测速度是Faster RCNN-ResNet-50网络的3倍,可以满足青皮核桃实时准确检测需求。该方法可为核桃果园智能化管理中的估产、采收规划等提供技术支撑,也可为近背景颜色的小果实实时准确检测提供思路。The aim of this research is to detect green walnuts in the natural environment using machine vision. The yield was also estimated to realize the intelligent management of orchards under fruiting detection. A number of challenges remained to identify the green-skinned walnuts at present, including the more complex environment in which the green walnuts grow, the small difference in color between green walnuts and leaves, as well as the small sizes of the green walnut fruit. An optimization strategy was then proposed to deal with these challenges during the green walnut detection. The images were also accurately captured to determine the walnut yields for the harvest planning. As such, deep learning was effectively used for the object detection of green walnut. A YOLOv3 network was selected to fully meet the actual requirements of real-time detection on the green walnut. An ablation experiment was performed on an improved YOLOv3 object detection algorithm. The quantitative and qualitative analyses were also utilized to verify the model under the optimization strategies. A pre-trained model was substantially improved to detect the green walnuts, compared with the ImageNet DET and COCO datasets. The results showed that the mean Average Precision(m AP) of green walnut detection was 83.55% after the YOLOv3 network training using the pre-trained model with the COCO dataset, and 86.11% for green walnut detection applying the COCO dataset pre-trained model network. The superior detection was achieved in the pre-training model using the COCO dataset for the single category detection. Ablation experiments demonstrated that there was an excellent performance in the green walnut detection using the data enhancement on dataset feature, with a 3.81 percentage points increase in the mAP of the model. The Mixup data enhancement was then added to promote the image data complexity for better detection performance, with a 1.24 percentage point increase in the mAP. The K-means clustering was applied to cluster the annotation boxes fo

关 键 词：图像处理 目标检测 算法 青皮核桃 YOLOv3 

分 类 号：S126[农业科学—农业基础科学]