基于深度学习的森林移动机器人树干检测

Trunk Detection for Forest Mobile Robots Based on Deep Learning

基于视觉导航的森林移动机器人具有机器人作为边缘设备算力有限、导航效果受光照影响较大的问题。为此,提出一种轻量化的树干检测方法,该方法基于YOLOv7-tiny模型,采用可见光图像与热成像图像作为输入,导航效果受光照影响较小;同时采用基于部分通道卷积(Partial Convolution,PConv)的特征提取模块-部分通道卷积高效层聚合网络(Partial Efficient Layer Aggregation Networks,P-ELAN),对基准模型进行轻量化改进;在训练阶段用alpha-CIoU损失函数替换原始的CIoU损失函数,提高边界框回归的准确性。结果表明,所提出的森林移动机器人树干检测方法相较于原始YOLOv7-tiny模型参数量减少31.7%,计算量减少33.3%,在图形处理器(Graphics Processing Unit,GPU)和中央处理器(Central Processing Unit,CPU)上的推理速度分别提升了33.3%和7.8%。修改后的模型在保持对树干检测精度基本不变的基础上更加轻量化,成为部署在机器人等边缘设备上的理想选择。

Forest mobile robots based on visual navigation face the problem of limited computational power as edge devices and the navigation performance is greatly affected by illumination.To address this,a lightweight trunk detection method is proposed.This method uses visible and thermal image as inputs,minimizing the impact of illumination on navigation performance it also employs a feature extraction module based on Partial Convolution(PConv)and a Partial Efficient Layer Aggregation Network(P-ELAN)to achieve lightweight improvements to the baseline model.During training,the alpha-CioU loss function is used to replace the original CIoU loss function,increasing the accuracy of bounding box regression.The results show that the proposed tree trunk detection method for forest mobile robots reduces the parameter count of the original YOLOv7-tiny model by 31.7%,decreases computation by 33.3%,and improves inference speeds on Graphics Processing Units(GPU)and Central Processing Units(CPU)by 33.3%and 7.8%.The modified model maintains comparable accuracy while being more lightweight,making it an ideal choice for deployment on edge devices such as robots.