GENERATING A GROUND PLANE FOR OBSTRUCTION DETECTION

1 . A method for detecting an obstruction as a farming machine traverses a field, the method comprising:

receiving, from an image acquisition system, image data of a field portion comprising a plurality of pixels, wherein each pixel in the plurality of pixels represents a three dimensional coordinate in the field;

applying an obstruction identification model to identify the obstruction in the field, wherein the obstruction identification model:

determines a seed segment for the field portion, the seed segment comprising a set of pixels with characteristics extrapolatable to the field portion;

determines a detection ground plane for the field portion by extrapolating characteristics of the seed segment to the three dimensional coordinates of the plurality of pixels, and

identifies the obstruction as a set of pixels of the plurality of pixels having three dimensional coordinates above the detection ground plane; and

executing an action such that the farming machine avoids the identified obstruction in the field.

2 . The method of claim 1 , wherein extrapolating the seed segment comprises:

extrapolating the characteristics of the seed segment to the field portion using a two dimensional representation of the three dimensional coordinates for the plurality of pixels.

3 . The method of claim 1 , wherein determining the seed segment comprises:

determining the seed segment from the image data as the farming machine moves through the field, wherein the set of pixels of the seed segment comprises received image data representing a plane.

4 . The method of claim 1 , wherein determining the seed segment comprises:

accessing the seed segment from a datastore, the accessed seed segment extrapolated from previously captured image data of one or more other fields and comprising a previously captured set of pixels comprising image data representing a plane.

5 . The method of claim 1 , wherein identifying the obstruction comprises:

removing pixels of the detection ground plane from the image data of the field portion;

segmenting the remaining image data into one or more buckets based on distance to the farming machine;

binning pixels of the remaining image data having three dimensional coordinates above the detection ground plane into the buckets; and

identifying buckets comprising binned pixels as obstructions.

6 . The method of claim 5 , wherein binning pixels into in the buckets comprises binning the pixels as one dimensional tubes.

7 . The method of claim 5 , wherein binning pixels comprises:

applying k-means clustering to the pixels in the remaining image data; and

adding clusters to the buckets.

8 . The method of claim 5 , wherein binning pixels comprises:

inputting binned pixels into a machine learning model, wherein the machine learning model classifies the binned pixels as static obstructions or dynamic obstructions and is trained on labelled image data of static and dynamic obstructions; and

labeling the binned pixels based on output from the machine learning model.

9 . The method of claim 5 , wherein binning pixels comprises:

receiving two or more sets of image data at periodic time intervals as the farming machine traverses the field;

binning pixels in each set of image data;

comparing locations of the binned pixels in each set of image data to determine if obstructions corresponding to the binned pixels are static or dynamic; and

labelling the binned pixels based on the comparison.

10 . The method of claim 1 , wherein the action comprises one or more of causing the farming machine to stop, slow down, or sound an alarm.

11 . The method of claim 1 , wherein executing the action such that the farming machine avoids the obstruction in the field comprises:

retrieving a path of the farming machine in the field;

replanning the path to avoid the obstruction in the field; and

instructing the farming machine to follow the replanned path.

12 . A non-transitory computer-readable storage medium storing instructions that when executed cause a processor to perform a method comprising:

receiving, from an image acquisition system, image data of a field portion comprising a plurality of pixels, wherein each pixel in the plurality of pixels represents a three dimensional coordinate in the field;

applying an obstruction identification model to identify the obstruction in the field, wherein the obstruction identification model:

determines a seed segment for the field portion, the seed segment comprising a set of pixels with characteristics extrapolatable to the field portion;

determines a detection ground plane for the field portion by extrapolating characteristics of the seed segment to the three dimensional coordinates of the plurality of pixels, and

identifies the obstruction as a set of pixels of the plurality of pixels having three dimensional coordinates above the detection ground plane; and

executing an action such that the farming machine avoids the identified obstruction in the field.

13 . The non-transitory computer-readable storage medium of claim 12 , wherein extrapolating the seed segment comprises:

extrapolating the characteristics of the seed segment to the field portion using a two dimensional representation of the three dimensional coordinates for the plurality of pixels.

14 . The non-transitory computer-readable storage medium of claim 12 , wherein determining the seed segment further comprises instructions that further cause the processor to perform a method comprising:

determining the seed segment from the image data as the farming machine moves through the field, wherein the set of pixels of the seed segment comprises received image data representing a plane.

15 . The non-transitory computer-readable storage medium of claim 12 , wherein determining the seed segment further comprises instructions that further cause the processor to perform a method comprising:

accessing the seed segment from a datastore, the accessed seed segment extrapolated from previously captured image data of one or more other fields and comprising a previously captured set of pixels comprising image data representing a plane.

16 . The non-transitory computer-readable storage medium of claim 12 , wherein identifying the obstruction further comprises instructions that further cause the processor to perform a method comprising:

removing pixels of the detection ground plane from the image data of the field portion;

segmenting the remaining image data into one or more buckets based on distance to the farming machine;

binning pixels of the remaining image data having three dimensional coordinates above the detection ground plane into the buckets; and

identifying buckets comprising binned pixels as obstructions.

17 . The non-transitory computer-readable storage medium of claim 16 , wherein binning pixels into in the buckets comprises binning the pixels as one dimensional tubes.

18 . The non-transitory computer-readable storage medium of claim 16 , wherein binning pixels further comprises instructions that further cause the processor to perform a method comprising:

applying k-means clustering to the pixels in the remaining image data; and

adding clusters to the buckets.

19 . The non-transitory computer-readable storage medium of claim 16 , wherein binning pixels further comprises instructions that further cause the processor to perform a method comprising:

inputting binned pixels into a machine learning model, wherein the machine learning model classifies the binned pixels as static obstructions or dynamic obstructions and is trained on labelled image data of static and dynamic obstructions; and

labeling the binned pixels based on output from the machine learning model.

20 . A computer system comprising:

a processor; and

a non-transitory computer-readable storage medium storing instructions that when executed cause the processor to perform actions comprising:

receiving, from an image acquisition system, image data of a field portion comprising a plurality of pixels, wherein each pixel in the plurality of pixels represents a three dimensional coordinate in the field;

applying an obstruction identification model to identify the obstruction in the field, wherein the obstruction identification model:

determines a seed segment for the field portion, the seed segment comprising a set of pixels with characteristics extrapolatable to the field portion;

determines a detection ground plane for the field portion by extrapolating characteristics of the seed segment to the three dimensional coordinates of the plurality of pixels, and

identifies the obstruction as a set of pixels of the plurality of pixels having three dimensional coordinates above the detection ground plane; and

executing an action such that the farming machine avoids the identified obstruction in the field.