SSD (Single Shot Detector) is one of the state-of-the-art object detection algorithms, and it combines high detection accuracy with real-time speed. However, it is widely recognized that SSD is less accurate in detecting small objects compared to large objects, because it ignores the context from outside the proposal boxes. In this paper, we present CSSD-a shorthand for context-aware single-shot multibox object detector. CSSD is built on top of SSD, with additional layers modeling multi-scale contexts. We describe two variants of CSSD, which differ in their context layers, using dilated convolution layers (DiCSSD) and deconvolution layers (DeCSSD) respectively. The experimental results show that the multi-scale context modeling significantly improves the detection accuracy. In addition, we study the relationship between effective receptive fields (ERFs) and the theoretical receptive fields (TRFs), particularly on a VGGNet. The empirical results further strengthen our conclusion that SSD coupled with context layers achieves better detection results especially for small objects (+3.2%AP @0.5 on MS-COCO compared to the newest SSD), while maintaining comparable runtime performance.
In addition, StairNet 12 and two variants of a context-aware single-shot detector REF were proposed to improve the detection performance of small objects.
Context-Aware Single-Shot Detector
2018 IEEE Winter Conference on Applications of Computer Vision (WACV)
2018 IEEE Winter Conference on Applications of Computer Vision (WACV)
Computer Science