  Object detection is a core problem in computer vision. With the development
of deep ConvNets, the performance of object detectors has been dramatically
improved. The deep ConvNets based object detectors mainly focus on regressing
the coordinates of bounding box, e.g., Faster-R-CNN, YOLO and SSD. Different
from these methods that considering bounding box as a whole, we propose a novel
object bounding box representation using points and links and implemented using
deep ConvNets, termed as Point Linking Network (PLN). Specifically, we regress
the corner/center points of bounding-box and their links using a fully
convolutional network; then we map the corner points and their links back to
multiple bounding boxes; finally an object detection result is obtained by
fusing the multiple bounding boxes. PLN is naturally robust to object occlusion
and flexible to object scale variation and aspect ratio variation. In the
experiments, PLN with the Inception-v2 model achieves state-of-the-art
single-model and single-scale results on the PASCAL VOC 2007, the PASCAL VOC
2012 and the COCO detection benchmarks without bells and whistles. The source
code will be released.
