Navigation line extraction algorithm for corn spraying robot based on
Light-YOLOv8s network
Abstract
The continuous and close combination of artificial intelligence
technology and agriculture promotes the rapid development of smart
agriculture, among which the agricultural robot navigation line
recognition algorithm based on deep learning has achieved great success
in detection accuracy and detection speed. However, there are still many
problems, such as the large size of the algorithm is difficult to deploy
in hardware equipment, and the accuracy and speed of crop row detection
in real farmland environment are low. In order to solve the above
problems, this paper proposed a navigation line extraction algorithm for
corn spraying robot based on Light-YOLOv8s network. Firstly, the
Convolution (Conv) module and C2f module of YOLOv8s network are replaced
with Depthwise Convolution (DWConv) module and PP-LCNet module
respectively to reduce the parameters (Params) and giga floating-point
operations per second (GFLOPs) of the network, so as to achieve the
purpose of network lightweight. Secondly, in order to reduce the
precision loss caused by network lightweight, the spatial pyramid
pooling fast (SPPF) module in the backbone network is changed to atrous
spatial pyramid pooling faster (ASPPF) module to improve the accuracy of
network feature extraction. Meanwhile, normalization-based attention
module (NAM) is introduced into the network to improve the network’s
attention to corn plants. Then the corn plant was located by using the
midpoint of the corn plant detection box. Finally, the least square
method is used to extract the maize crop row line, and the middle line
of the maize crop row line is the navigation line of the maize spraying
robot. According to the experimental results, the Params of
Light-YOLOv8s network decreased by 29.24%, 86.64% and 55.38%,
respectively, compared with YOLOv5s network, YOLOv7 network and YOLOv8s
network. GFLOPs dropped 26.79%, 88.77%, and 58.74%, respectively,
while accuracy lost only 1%, 0.6%, and 2.2%. It shows that the
Light-YOLOv8s network proposed in this paper greatly reduces the size of
the model, solves the problems such as the difficulty of deployment
caused by the large size of the existing algorithm, and also greatly
reduces the accuracy loss of the model, and solves the problems such as
the reduced accuracy of the algorithm caused by the lightweight network.
When the corn spraying robot works in the real farmland environment, the
navigation line extraction algorithm proposed in this paper not only
ensures the real-time navigation of the corn spraying robot, but also
ensures the accuracy of the navigation, and makes a contribution to the
development of agricultural robot visual navigation technology.