Abstract
Aiming at the problem of low intelligence in the automatic navigation of the cuttage and film covering multi-functional machine for low tunnels, this study proposed a navigation line extraction method based on the improved YOLOv5s model, which can achieve the accurate extraction of navigation lines based on two planting methods of seedling transplanting and direct seeding. Firstly, we pre-processed the acquired images using inverse perspective transformation. Next, the Coordinate Attention and Ghost modules were applied to improve the YOLOv5s architecture, increasing the detection accuracy and speed of field targets. Finally, we extracted the feature points and fit the navigation lines based on the shape features of the targets using the geometric method. The experimental results showed that, compared with other algorithms, the accuracy of the proposed algorithm could reach more than 96%, the accuracy of navigation line extraction reached 98%, and the average detection time was 51 ms. The proposed method was robust and universal, and it can provide reliable navigation paths for the cuttage and film covering multi-functional machine.
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