Mask-guided SSD for small-object detection

Detecting small objects is a challenging job for the single-shot multibox detector (SSD) model due to the limited information contained in features and complex background interference. Here, we increased the performance of the SSD for detecting target objects with small size by enhancing detection features with contextual information and introducing a segmentation mask to eliminate background regions. The proposed model is referred to as a “guided SSD” (Mask-SSD) and includes two branches: a detection branch and a segmentation branch. We created a feature-fusion module to allow the detection branch to exploit contextual information for feature maps with large resolution, with the segmentation branch primarily built with atrous convolution to provide additional contextual information to the detection branch. The input of the segmentation branch was also the output of the detection branch, and output segmentation features were fused with detection features in order to classify and locate target objects. Additionally, segmentation features were applied to generate the mask, which was utilized to guide the detection branch to find objects in potential foreground regions. Evaluation of Mask-SSD on the Tsinghua-Tencent 100K and Caltech pedestrian datasets demonstrated its effectiveness at detecting small objects and comparable performance relative to other state-of-the-art methods.