基于AutoAssign模型的水果检测
Fruit detection based on AutoAssign model
丁士宁 1刘金兰1
作者信息
- 1. 信阳农林学院信息工程学院,信阳 464000
- 折叠
摘要
为提高水果检测精度,收集菠萝、蛇果、火龙果、香蕉四种水果的图像,构建水果检测数据集.将Auto-Assign模型应用到水果检测中,并在此基础上提出了改进.采用多尺度训练策略,并用RegNet网络替代原有的骨干特征提取网络ResNet50.改进模型的平均精度均值达到86.8%,比原AutoAssign模型提升了5.4个百分点.所提模型的平均帧率达到34.6 img·s-1.与Faster R-CNN、RetinaNet、FCOS模型相比,该模型的平均精度均值分别提升了6.0、5.7、5.0个百分点.所提模型明显提升了水果检测精度,检测速度可接受,对于水果检测具有一定的参考意义.
Abstract
To improve the accuracy of fruit detection,four types of fruit images,which is pineapple,snake fruit,dragon fruit,and banana,were collected to construct a fruit detection dataset.The AutoAssign model is applied to fruit detection and improve-ments is proposed based on this model.Multi-scale training strategy is adopted,and RegNet network is used to replace ResNet50 network which is the original backbone feature extraction network.The mean average precision of the improved model reached 86.8%,which is 5.4 percentage points higher than the original AutoAssign model.And the frame per second of the proposed model achieves 34.6 img·s-1.Compared with Faster R-CNN,RetinaNet,and FCOS models,the mean average precision of this model has increased by 6.0,5.7,and 5.0 percentage,respectively.The proposed model significantly improves the accuracy of fruit detection,and the detection speed is acceptable,which has certain reference significance for fruit detection.
关键词
水果检测/AutoAssign/多尺度训练/RegNet网络Key words
fruit detection/AutoAssign/multi-scale training/RegNet network引用本文复制引用
基金项目
信阳农林学院青年教师科研基金项目(QN2021057)
出版年
2024
NETL
NSTL
万方数据