| Summary: | The multi-scale object detection, especially small object detection, is still a challenging task. This paper proposes an improved multi-scale object detection network based on single shot multibox detector (SSD), and the network is named as SSD-MSN. The SSD-MSN can learn more rich features of small objects from the enlarged areas, which are clipped from the raw image. The extra features are contributed to improving detection performance. The SSD-MSN includes two subnets: area proposal network (APN) and multi-scale object detection network, namely SSD detector. The APN is used to select the area proposals containing one or more objects from clipped areas. The SSD detector is used to predict the classification and location of objects from raw image and area proposals. Besides, a valid dividing image strategy is introduced in this paper, which can generate 3*3 clipped areas from the raw image. The strategy not only generates more area proposals but also ensures more objects can be contained in each clipped area. It plays the role of data augmentation, which is critical to detection performance. The experiment results on PASCAL VOC and COCO show that SSD-MSN achieves state-of-the-art detection performance and improves the multi-scale object detection performance effectively.
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