  Object detection has made great progress in the past few years along with the
development of deep learning. However, most current object detection methods
are resource hungry, which hinders their wide deployment to many resource
restricted usages such as usages on always-on devices, battery-powered low-end
devices, etc. This paper considers the resource and accuracy trade-off for
resource-restricted usages during designing the whole object detection
framework. Based on the deeply supervised object detection (DSOD) framework, we
propose Tiny-DSOD dedicating to resource-restricted usages. Tiny-DSOD
introduces two innovative and ultra-efficient architecture blocks: depthwise
dense block (DDB) based backbone and depthwise feature-pyramid-network (D-FPN)
based front-end. We conduct extensive experiments on three famous benchmarks
(PASCAL VOC 2007, KITTI, and COCO), and compare Tiny-DSOD to the
state-of-the-art ultra-efficient object detection solutions such as Tiny-YOLO,
MobileNet-SSD (v1 & v2), SqueezeDet, Pelee, etc. Results show that Tiny-DSOD
outperforms these solutions in all the three metrics (parameter-size, FLOPs,
accuracy) in each comparison. For instance, Tiny-DSOD achieves 72.1% mAP with
only 0.95M parameters and 1.06B FLOPs, which is by far the state-of-the-arts
result with such a low resource requirement.
