Spatial Location of Sugarcane Node for Binocular Vision-Based Harvesting Robots Based on Improved YOLOv4

Abstract

Spatial location of sugarcane nodes using robots in agricultural conditions is a challenge in modern precision agriculture owing to the complex form of the sugarcane node when wrapped with leaves and the high computational demand. To solve these problems, a new binocular location method based on the improved YOLOv4 was proposed in this paper. First, the YOLOv4 deep learning algorithm was improved by the Channel Pruning Technology in network slimming, so as to ensure the high recognition accuracy of the deep learning algorithm and to facilitate transplantation to embedded chips. Secondly, the SIFT feature points were optimised by the RANSAC algorithm and epipolar constraint, which greatly reduced the mismatching problem caused by the similarity between stem nodes and sugarcane leaves. Finally, by using the optimised matching point to solve the homography transformation matrix, the space location of the sugarcane nodes was for the first time applied to the embedded chip in the complex field environment. The experimental results showed that the improved YOLOv4 algorithm reduced the model size, parameters and FLOPs by about 89.1%, while the average precision (AP) of stem node identification only dropped by 0.1% (from 94.5% to 94.4%). Compared with other deep learning algorithms, the improved YOLOv4 algorithm also has great advantages. Specifically, the improved algorithm was 1.3% and 0.3% higher than SSD and YOLOv3 in average precision (AP). In terms of parameters, FLOPs and model size, the improved YOLOv4 algorithm was only about 1/3 of SSD and 1/10 of YOLOv3. At the same time, the average locational error of the stem node in the Z direction was only 1.88 mm, which totally meets the demand of sugarcane harvesting robots in the next stage.

Keywords

• sugarcane node
• binocular vision
• spatial location
• YOLOv4
• channel pruning