  There are still two problems in SDD causing some inaccurate results: (1) In
the process of feature extraction, with the layer-by-layer acquisition of
semantic information, local information is gradually lost, resulting into less
representative feature maps; (2) During the Non-Maximum Suppression (NMS)
algorithm due to inconsistency in classification and regression tasks, the
classification confidence and predicted detection position cannot accurately
indicate the position of the prediction boxes. Methods: In order to address
these aforementioned issues, we propose a new architecture, a modified version
of Single Shot Multibox Detector (SSD), named Precise Single Stage Detector
(PSSD). Firstly, we improve the features by adding extra layers to SSD.
Secondly, we construct a simple and effective feature enhancement module to
expand the receptive field step by step for each layer and enhance its local
and semantic information. Finally, we design a more efficient loss function to
predict the IOU between the prediction boxes and ground truth boxes, and the
threshold IOU guides classification training and attenuates the scores, which
are used by the NMS algorithm. Main Results: Benefiting from the above
optimization, the proposed model PSSD achieves exciting performance in
real-time. Specifically, with the hardware of Titan Xp and the input size of
320 pix, PSSD achieves 33.8 mAP at 45 FPS speed on MS COCO benchmark and 81.28
mAP at 66 FPS speed on Pascal VOC 2007 outperforming state-of-the-art object
detection models. Besides, the proposed model performs significantly well with
larger input size. Under 512 pix, PSSD can obtain 37.2 mAP with 27 FPS on MS
COCO and 82.82 mAP with 40 FPS on Pascal VOC 2007. The experiment results prove
that the proposed model has a better trade-off between speed and accuracy.
