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SSD (Single Shot Multibox Detector) is one of the most successful objectdetectors for its high accuracy and fast speed. However, the features fromshallow layer (mainly Conv4_3) of SSD lack semantic information, resulting inpoor performance in small objects. In this paper, we proposed DDSSD (Dilationand Deconvolution Single Shot Multibox Detector), an enhanced SSD with a novelfeature fusion module which can improve the performance over SSD for smallobject detection. In the feature fusion module, dilation convolution module isutilized to enlarge the receptive field of features from shallow layer anddeconvolution module is adopted to increase the size of feature maps from highlayer. Our network achieves 79.7% mAP on PASCAL VOC2007 test and 28.3% mmAP onMS COCO test-dev at 41 FPS with only 300x300 input using a single Nvidia 1080GPU. Especially, for small objects, DDSSD achieves 10.5% on MS COCO and 22.8%on FLIR thermal dataset, outperforming a lot of state-of-the-art objectdetection algorithms in both aspects of accuracy and speed.

Detecting Small Objects in Thermal Images Using Single-Shot Detector