A lightweight network for improving wheat ears detection and counting based on YOLOv5s

• 全文链接

利用YOLOv5s改进小麦穗检测计数的轻量级网络

关键词：

来源：

Frontiers

全文链接：

//agri.nais.net.cn/topic/downloadFile/a2f1e952-a90c-4900-9eac-f60831d72b94

来源地址：

https://www.frontiersin.org/articles/10.3389/fpls.2023.1289726/full

资源所属：

小麦遗传育种专题

类型：

前沿资讯

语种：

英语

原文发布日期：

2023-12-18

摘要：

This study proposes a lightweight method for detecting and counting wheat ears based on YOLOv5s. It utilizes the ShuffleNetV2 lightweight convolutional neural network to optimize the YOLOv5s model by reducing the number of parameters and simplifying the complexity of the calculation processes. In addition, a lightweight upsampling operator content-aware reassembly of features is introduced in the feature pyramid structure to eliminate the impact of the lightweight process on the model detection performance. This approach aims to improve the spatial resolution of the feature images, enhance the effectiveness of the perceptual field, and reduce information loss. Finally, by introducing the dynamic target detection head, the shape of the detection head and the feature extraction strategy can be dynamically adjusted, and the detection accuracy can be improved when encountering wheat ears with large-scale changes, diverse shapes, or significant orientation variations.