4. A method as comprising: receiving an input image; generating a convolutional feature map; identifying, by a first type of neural network, a candidate object in the input image, wherein the identifying the candidate object in the input image comprises: generating one or more anchors at a point of the input image; determining an overlap of individual ones of the one or more anchors to a ground-truth box; assigning a label to each anchor of the one or more anchors based at least in part on the overlap; assigning a score to the label based at least in part on the overlap; and identifying the candidate object at the point based at least in part on the score; determining, by a second type of neural network, a category of the candidate object, wherein the first type of neural network and the second type of neural network share at least one algorithm; and assigning a confidence score to the category of the candidate object.
4. A method as comprising: receiving an input image; generating a convolutional feature map; identifying, by a first type of neural network, a candidate object in the input image, wherein the identifying the candidate object in the input image comprises: generating one or more anchors at a point of the input image; determining an overlap of individual ones of the one or more anchors to a ground-truth box; assigning a label to each anchor of the one or more anchors based at least in part on the overlap; assigning a score to the label based at least in part on the overlap; and identifying the candidate object at the point based at least in part on the score; determining, by a second type of neural network, a category of the candidate object, wherein the first type of neural network and the second type of neural network share at least one algorithm; and assigning a confidence score to the category of the candidate object. 5. A method as claim 4 recites, wherein the identifying the candidate object in the input image further comprises: identifying an anchor corresponding to a highest score, the highest score corresponding to a percentage of the overlap; shifting the anchor corresponding to the highest score to better define the candidate object; and scaling the anchor corresponding to the highest score to better define the candidate object.