Deep neural network architecture for image segmentation

A non-transitory computer readable medium including instructions that when executed by a process are configured to:

• identify at least a portion of an image collected at a vehicle;
• applying a first convolutional neural network to the at least the portion of the image at a first stage;
• pooling, at a second stage, a plurality of subregion representations from an output of the first convolutional neural network for the first stage;
• performing, at a third stage, at least one convolution of an output of the second stage;
• performing, at a fourth stage, a first deconvolution from the output of the first stage and a second deconvolution from the output of the second stage;
• performing, at the fourth stage, a third deconvolution from an output of the third stage;
• concatenating, at a fifth stage, the output of the fourth stage and the output of the third stage;
• applying a second convolutional neural network to the output of the fifth stage;
• and
• classifying the at least the portion of the image as a road object category in response to an output of the second convolutional neural network.

An apparatus for encoding objects in a camera-captured image with a deep neural network pipeline, the method comprising:

• at least one processor;
• and
• at least one memory including computer program code for one or more programs, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following:
• identifying at least a portion of the camera-captured image;
• applying a first convolutional neural network to the at least the portion of the camera-captured image at a first stage;
• pooling, at a second stage, a plurality of subregion representations from an output of the first convolutional neural network for the first stage;
• performing, at a third stage, at least one convolution of an output of the second stage;
• performing, at a fourth stage, a first deconvolution from the output of the first stage, a second deconvolution from the output of the second stage, and a third deconvolution from an output of the third stage;
• concatenating, at a fifth stage, the output of the fourth stage and the output of the third stage;
• applying a second convolutional neural network to the output of the fifth stage;
• and
• classifying the at least the portion of the camera-captured image as an object category in response to an output of the second convolutional neural network.