1. A method to detect an object in an image, the method comprising: receiving an input image; inputting the input image into a fully convolutional neural network (FCN) without requiring a pre-processing step to convert the input image into a canonical size, the FCN comprising: a series of convolution layers and pooling layers that generate feature maps; and an upsampling layer that provides input to a first set of convolution layers to detect confidence scores and a second set of convolution layers to detect position; an output feature map that comprises confidence scores and corresponding positions; converting the output feature map to bounding boxes, each bounding box being associated with a position in the input image and a confidence score for containing the object within the bounding box; and outputting a set of bounding boxes having confidence scores that meet a threshold level that classifies the object as being contained in each bounding box.