Study of convolutional neural network-based semantic segmentation methods on edge intelligence devices for field agricultural robot navigation line extraction

Smart agricultural machinery is emerging as the future trend for field robots, and the fully automatic robot has a great application prospect. However, it is a big challenge for robots to navigate in complex farmland environments. In this research, 5 deep learning-based computer vision methods under different field scenes for field navigation line extraction were studied and successfully deployed on an embedded system, which can be integrated into robots for automatic navigation in the future. The field road was segmented by the semantic segmentation algorithm at first, and then the navigation line is extracted from the segmented image by a polygon fitting method. Finally, all the models are transformed through the TensorRT library and deployed on the edge computing device Jetson Nano. In the experiment, five reprehensive semantic segmentation networks namely UNet, Deeplabv3+, BiseNetv1, BiseNetv2, and ENet networks were selected. Among the five networks, Deeplabv3+ is the most accurate. In five scenes, its average segmentation accuracy is 84.87 %, and the navigation line error is 9.59 pixels. Especially in the third scene with shadow and occlusion, it performs best, with only 8.34 pixel error, But the speed of Deeplabv3+ is only 9.7 FPS. ENet, BiseNetv1, and BiseNetv2 are lightweight networks. The speed of ENet is 16.8 FPS, BiseNetv2 is 17 FPS, and BiseNetv1 is 15.8 FPS. In segmentation accuracy and navigation line error, ENet performs better than BiseNet series networks, which are 84.94 % and 10.73 pixels, respectively. In the third scene with shadow and occlusion, it also performs slightly better than BiseNet series networks. In summary, deep learning-based semantic segmentation methods have strong robustness and stability in complex environment compared with previous research. Among all currently available neural networks, ENet has the best performance and good application potential in field navigation.