  Object detection plays an important role in various visual applications.
However, the precision and speed of detector are usually contradictory. One
main reason for fast detectors' precision reduction is that small objects are
hard to be detected. To address this problem, we propose a multiple receptive
field and small-object-focusing weakly-supervised segmentation network
(MRFSWSnet) to achieve fast object detection. In MRFSWSnet, multiple receptive
fields block (MRF) is used to pay attention to the object and its adjacent
background's different spatial location with different weights to enhance the
feature's discriminability. In addition, in order to improve the accuracy of
small object detection, a small-object-focusing weakly-supervised segmentation
module which only focuses on small object instead of all objects is integrated
into the detection network for auxiliary training to improve the precision of
small object detection. Extensive experiments show the effectiveness of our
method on both PASCAL VOC and MS COCO detection datasets. In particular, with a
lower resolution version of 300x300, MRFSWSnet achieves 80.9% mAP on VOC2007
test with an inference speed of 15 milliseconds per frame, which is the
state-of-the-art detector among real-time detectors.
