Multi-flow Sub-network and Multiple Connections for Single Shot Detection

One-stage object detection methods are usually more computationally efficient than two-stage methods, which makes it more likely to be applied in practice. However, one-stage methods often suffer from lower detection accuracies, especially when the objects to be detected are small. In this paper, we propose a multi-flow sub-network and multiple connections for single shot detection (MSSD), which is built upon a one-stage strategy to inherit the computational efficiency and improve the detection accuracy. The multi-flow sub-network in MSSD aims to extract high quality feature maps with high spatial resolution, sufficient non-linear transformation, and multiple receptive fields, which facilitates detection of small objects in particular. In addition, MSSD uses multiple connections, including up-sampling, down-sampling, and resolution-invariant connections, to combine feature maps of different layers, which helps the model capture fine-grained details and improve feature representation. Extensive experiments on PASCAL VOC and MS COCO demonstrate that MSSD achieves competitive detection accuracy with high computational efficiency compared to state-of-the-art methods. MSSD with input size of 320\(\,\times \,\)320 achieves 80.6% mAP on VOC2007 at 45 FPS and 29.7% mAP on COCO, both with a Nvidia Titan X GPU.