US 12,008,816 B2
Method and system for real time object detection
Vipin Sharma, Haryana (IN); Akshat Agrawal, Haryana (IN); Jitesh Kumar Singh, Karnataka (IN); and Arpit Awasthi, Haryana (IN)
Assigned to HL KLEMOVE CORP., Incheon (KR)
Filed by HL Klemove Corp., Pyeongtaek (KR)
Filed on Dec. 1, 2021, as Appl. No. 17/540,172.
Claims priority of application No. 202011052691 (IN), filed on Dec. 3, 2020.
Prior Publication US 2022/0180107 A1, Jun. 9, 2022
Int. Cl. G06V 20/56 (2022.01); G06F 18/24 (2023.01); G06N 3/04 (2023.01); G06V 10/32 (2022.01); G06V 10/40 (2022.01); G06V 10/94 (2022.01)
CPC G06V 20/56 (2022.01) [G06F 18/24 (2023.01); G06N 3/04 (2013.01); G06V 10/32 (2022.01); G06V 10/40 (2022.01); G06V 10/95 (2022.01)] 11 Claims
OG exemplary drawing
 
1. A method for real-time object detection for a host vehicle, the method comprising:
capturing an image in vicinity of the host vehicle;
feeding the captured image to a deep fully convolution neural network;
extracting one or more relevant features from the captured image;
classifying the extracted features using one or more branches of the deep fully convolution neural network to identify different size of objects, each of the one or more branches comprising a different receptive field corresponding to the size of the object;
predicting objects present in the image based on a predetermined confidence threshold;
marking the predicted objects in the image; and
plotting the marked image on a display,
wherein the different receptive field is created by performing a different number of down sampling and a different number of depth-wise separable convolution to the extracted features.