基于改进YOLOv7的水果目标检测方法
Fruit Target Detection Method based on Improved YOLOv7
刘麒 1李奎东 1常广良 2王影1
作者信息
- 1. 吉林化工学院信息与控制工程学院,吉林吉林 132022
- 2. 科世达(长春)汽车电器有限公司,吉林长春 130031
- 折叠
摘要
目前水果种植广泛,但水果分类工作大多数是人工完成,耗费了大量人工成本,少数机器识别也有速度慢、准确率低等问题.针对此类目标检测识别效率较低等问题,提出了一种基于改进YOLOv7算法的水果目标检测算法,通过引入ECA注意力机制,增强通道维度的关联性,提升了模型的表达能力和学习效果;使用PConv代替了部分的卷积结构,同时削减冗余计算和内存访问,更有效地提取空间特征;损失函数使用了 MPDIoU,针对传统IoU增强了梯度可导性,便于进行图像分割任务的训练和优化.改进的YOLOv7算法在精度和平均精度均值方面分别提升了 4％和3％,能准确识别出水果.
Abstract
At present,fruits are widely planted,but most of the fruit classification is done manually,which consumes a lot of labor costs,and a few machine recognition also has problems such as slow speed and low accuracy.To solve the problem of low efficiency of target detection and recognition,a fruit target detection algorithm based on improved YOLOv7 algorithm was proposed.By introducing ECA attention mechanism,the relevance of channel dimensions was enhanced,and the expression ability and learning effect of the model were improved;PConv was used instead of partial convolution structure,and redundant computing and memory access were reduced to extract spatial features more effectively;The loss function uses MPDIoU,which enhances the gradient differentiability for traditional IoU,facilitating the training and optimization of image segmentation tasks.The improved YOLOv7 algorithm can accurately recognize fruits by improving the precision and average accuracy by 4％and 3％respectively.
关键词
水果识别/目标检测/YOLOv7/MPDIoUKey words
fruit recognition/object detection/YOLOv7/MPDIoU引用本文复制引用
出版年
2024
NETL
NSTL
万方数据