Detection of inter row navigation lines in corn fields based on improved CenterNet

In complex corn orchard environments, traditional machine vision algorithms often struggle to handle changes in light, occlusion between crops, and interference from weeds, which leads to inaccurate navigation line extraction. This article proposes a corn field navigation route detection method based on improved CenterNet to address this issue. Firstly, we replaced the CenterNet backbone network with ResNeSt, which can effectively improve detection performance when facing small targets and complex hierarchical image content. At the same time, we added CBMA attention mechanism in CenterNet to enhance the recognition of target features of corn seedlings and weaken background interference. We use the midpoint of corn seedlings as the navigation positioning base point, use an improved CenterNet model to detect corn seedlings on both sides, use the center point of corn seedlings as the positioning reference point, and then use the least squares method to fit the center points of corn seedlings on both sides. The experimental results show that the improved CenterNet model has a detection accuracy of 76.44% and a detection speed of 42.09 frames per second. Compared with the original CenterNet model, it has improved by 4.28 percentage points and 1.51 frames per second, respectively. From this, it can be seen that the model can accurately identify corn seedlings, and the average processing time for one frame of image is 0.082 seconds, which fully meets the needs of navigation in corn fields.