Method and system for real time object detection
AssigneeHl Klemove Corp.
InventorsVipin Sharma, Akshat Agrawal, Jitesh Kumar Singh, Arpit Awasthi
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Abstract
The present disclosure relates to a method for real-time object detection, the method comprising: capturing an image in vicinity of a 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 to identify different size of objects; predicting objects present in the image based on a predetermined confidence threshold; and marking the predicted objects in the image.
Claims11
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.
A system for real-time object detection for a host vehicle, comprising:
- an image sensor configured to capture an image in the vicinity of the host vehicle;
- a controller communicatively connected to the image sensor and configured to:
- obtain the captured from the image sensor;
- feed the captured image to a deep fully convolution neural network;
- extract one or more relevant features from the captured image;
- classify the extracted features using one or more branches of the deep fully convolution neural network to identify different size of objects, each of the or more branches comprising a different receptive field corresponding to the size of the object;
- predict objects present in the image based on a predetermined confidence threshold;
- mark the predicted objects in the image;
- and
- plot 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.
Dependencies are inferred from the claim text.