关键词: 深度特征融合, 小目标检测, 深度学习, 自动驾驶场景

Abstract:

In order to improve the detection performance of one-stage object detection algorithm for small objects and overlapping objects, and make it applicable to automatic driving scenarios, a deep feature fusion algorithm DFSSD based on SSD（Single Shot Multibox Detector） is proposed. DFSSD mainly improves the SSD algorithm from two aspects：on one hand, an efficient feature fusion method is adopted to enhance the feature expression ability of the model and the detection ability for difficult small objects without introducing extra parameters and excessive computation. On the other hand, a training method with noise is introduced, that is, during the training, the unmarked hard positive samples（undistinguishable positive examples） are randomly added to the training, so as to improve the anti-interference ability of the algorithm to complex background and reduce false detection rate of small difficult objects. On the PASCAL VOC2007 test set, compared with SSD300, DFSSD300’s mAP（mean Average Precision） increases by 3.7 percentage points, and on the KITTI dataset, the AP（Average Precision）  of  difficult objects in car class increases by 5 percentage points. At the same time, it has the same detection speed as SSD300.

Key words: deep feature fusion, small object detection, deep learning, automatic driving scene