关键词: 单次多框目标检测器（SSD）模型, 多尺度特征融合, 目标检测, 深度学习

Abstract: In this paper, it is proposed that the accuracy of small and medium objects detection of SSD (single shot multibox detector) can be improved by incorporating a modified multi-scale convolution feature fusion method. In order to enhance small objects detection, the region magnification extraction on the low-layer feature maps is adopted. Then, features are extracted from the high-layer feature maps to make the detection of medium objects better. These features are finally fused by the multi-scale convolution detection method as in the original SSD architecture. Moreover, the parameters of the present model are obtained via the parameter retraining. On the MS COCO test set, the present model shows that the mAP (mean average precision) of medium and small objects decetion is 75.1% and 40.5% respectively, which is nearly 16.3% and 23.1% higher than the original SSD.

Key words: single shot multibox detector (SSD), multi-scale feature fusion, object detection, deep-learning