  The recent advances of compressing high-accuracy convolution neural networks
(CNNs) have witnessed remarkable progress for real-time object detection. To
accelerate detection speed, lightweight detectors always have few convolution
layers using single-path backbone. Single-path architecture, however, involves
continuous pooling and downsampling operations, always resulting in coarse and
inaccurate feature maps that are disadvantageous to locate objects. On the
other hand, due to limited network capacity, recent lightweight networks are
often weak in representing large scale visual data. To address these problems,
this paper presents a dual-path network, named DPNet, with a lightweight
attention scheme for real-time object detection. The dual-path architecture
enables us to parallelly extract high-level semantic features and low-level
object details. Although DPNet has nearly duplicated shape with respect to
single-path detectors, the computational costs and model size are not
significantly increased. To enhance representation capability, a lightweight
self-correlation module (LSCM) is designed to capture global interactions, with
only few computational overheads and network parameters. In neck, LSCM is
extended into a lightweight crosscorrelation module (LCCM), capturing mutual
dependencies among neighboring scale features. We have conducted exhaustive
experiments on MS COCO and Pascal VOC 2007 datasets. The experimental results
demonstrate that DPNet achieves state-of the-art trade-off between detection
accuracy and implementation efficiency. Specifically, DPNet achieves 30.5% AP
on MS COCO test-dev and 81.5% mAP on Pascal VOC 2007 test set, together mwith
nearly 2.5M model size, 1.04 GFLOPs, and 164 FPS and 196 FPS for 320 x 320
input images of two datasets.
