# RBF-Based Monocular Vision Navigation for Small Vehicles in Narrow Space below Maize Canopy
## 2 Materials and Methods
### Navigation Method
#### 2.2.3 Image Recognition  
Here, we first explain the vehicle's working environment. The vehicle moves between the crop rows. There is about a 70 cm gap between crop rows, and the width of the vehicle is about 55 cm, as Figure 5 shows. In ideal conditions, clearances on both sides of the vehicle are about 5-8 cm. Figure 6 shows the division of the area in front of the vehicle. When A, B, C, D regions have no stalk, it can be passed easily. When F and C (or B and E) regions have stalks, then the vehicle processes through a complex path planning method, so that the vehicle may bypass the C area (B area) of crops, and finally return to the correct crop row. Conventional agriculture monocular vision navigation methods often use a method of extracting straight crop rows. First, we can find the boundary of the passable area between rows by using the image segmentation method. Then, the center line as a navigational index can be calculated. This method has larger error, ignoring crops outside the crop row, resulting in collision between the vehicle and crops. In addition, navigating along this line in such a high density planting will result in larger heading deflection, so that the next frame of the navigational index extraction error increases. Therefore, in such a confined environment it is necessary to extract the navigational index to identify all of the stalks, find a path with the smallest collision probability from all of the stalks, and reduce the change of heading.  
Figure 4: Transformation between data from the image and LIDAR. (**a**) Image points in camera coordinates; and (**b**) absolute coordinates in the world coordinate system.  
Figure 3: Transformation between the world coordinate and the camera coordinate.  
Image recognition is realized mainly through the following steps:  
1. RGB threshold analysis: the target stalk image region and other regional RGB values are separated, and the threshold range image is determined.
2. Otsu threshold analysis: this study uses the Otsu algorithm for image binarization. The threshold of the method is used to maximize the class variance between the foreground and background. This method is very sensitive to noise and target size and has better image segmentation results for the variance between two classes of a unimodal image, as shown in Figure 7.
3. Analysis of the image block filter: the domain neighbor segmentation method is used to filter out noise points caused by the binarization processing of the weeds and corn stubble of the previous quarter image: \[N_{4}=(x,y)\cup(x,y-1),(x+1,y),(x,y+1),(x-1,y)\] (3)  
The area of each connected region of the image is calculated through the method of neighbor domains, according to the area in descending order. The image blocks are less than 50 pixels, by experience, which is regarded as the standard for noise elimination.  
After removing the isolated noise points, the interference of weeds and other extracted stalks is filtered out. However, maize leaves and weeds are similar to the stalk in the image block, and they still cause significant interference in the recognition of stalks. As Figure 7b shows, the shapes of maize leaves and weeds are irregular, but that of stalks is elongated.  
Figure 5: The vehicle’s moving environment.  
Figure 6: The division of the area in front of the vehicle.  
The system uses tiles of an external rectangular box for further noise removal. All of the tiles of the circumscribed rectangles are obtained, and the aspect ratio of the external rectangle is used as the basis for assessment. An aspect ratio greater than five is marked as the maize stalk, and the other shapes are defined as noise. In Figure 7c, the solid black image represents the stalk, and the hollow block represents the noise to be removed. In Figure 7d, the stalk independently calculated for each block is used to extract the ridge line. The lowest point of the ridge line is marked as the coordinate point of each stalk.