  How can a single fully convolutional neural network (FCN) perform on object
detection? We introduce DenseBox, a unified end-to-end FCN framework that
directly predicts bounding boxes and object class confidences through all
locations and scales of an image. Our contribution is two-fold. First, we show
that a single FCN, if designed and optimized carefully, can detect multiple
different objects extremely accurately and efficiently. Second, we show that
when incorporating with landmark localization during multi-task learning,
DenseBox further improves object detection accuray. We present experimental
results on public benchmark datasets including MALF face detection and KITTI
car detection, that indicate our DenseBox is the state-of-the-art system for
detecting challenging objects such as faces and cars.
