US 12,112,536 B1
Camera apparatus and method of training and operating neural network model for enhanced foliage detection
Dhivakar Kanagaraj, Karur (IN); Pranav M P, Bengaluru (IN); Raghul Raghu, Madurai (IN); and Prakash Mathews Pothen, Thiruvalla (IN)
Assigned to Tartan Aerial Sense Tech Private Limited, Bangalore (IN)
Filed by Tartan Aerial Sense Tech Private Limited, Bangalore (IN)
Filed on Dec. 29, 2023, as Appl. No. 18/401,066.
Claims priority of application No. 202341071593 (IN), filed on Oct. 19, 2023.
Int. Cl. G06V 20/10 (2022.01); G06T 5/40 (2006.01); G06T 5/70 (2024.01); G06T 7/11 (2017.01); G06T 7/136 (2017.01); G06V 10/26 (2022.01); G06V 10/77 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01)
CPC G06V 20/188 (2022.01) [G06T 5/40 (2013.01); G06T 5/70 (2024.01); G06T 7/11 (2017.01); G06T 7/136 (2017.01); G06V 10/273 (2022.01); G06V 10/7715 (2022.01); G06V 10/7747 (2022.01); G06V 10/82 (2022.01); G06T 2207/10024 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30188 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A camera apparatus, comprising:
control circuitry, wherein in a training phase, the control circuitry is configured to:
generate a set of binary images of foliage masks, wherein each binary image comprises one or more foliage regions demarcated from a background non-foliage region;
generate a modified training dataset of color images from a first set of input color images; and
train a custom neural network model for foliage detection based on the generated set of binary images of foliage masks and the modified training dataset,
wherein in the training phase, the control circuitry causes the custom neural network model to:
learn a plurality of features related to foliage from the modified training dataset;
further learn a color variation range of a predefined color associated with the plurality of features; and
utilize a combination of the plurality of features related to foliage and the color variation range of the predefined color to obtain a trained custom neural network model, and
wherein in an operational phase, the control circuitry is further configured to:
capture a new color image of an agricultural field;
remove a portion of the new color image, wherein the portion comprises pixels indicative of an artificial object in a field-of-view (FOV) of the camera apparatus; and
operate the trained custom neural network model to detect one or more foliage regions in the new color image in a real time or near real time.