Scale-Aware Trident Networks for Object Detection

Yanghao Li; Yuntao Chen; Naiyan Wang; Zhaoxiang Zhang

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arXiv · 1901.01892

Scale-Aware Trident Networks for Object Detection

Yanghao Li·Yuntao Chen·Naiyan Wang·Zhaoxiang Zhang

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Abstract

Scale variation is one of the key challenges in object detection. In this work, we first present a controlled experiment to investigate the effect of receptive fields for scale variation in object detection. Based on the findings from the exploration experiments, we propose a novel Trident Network (TridentNet) aiming to generate scale-specific feature maps with a uniform representational power. We construct a parallel multi-branch architecture in which each branch shares the same transformation parameters but with different receptive fields. Then, we adopt a scale-aware training scheme to specialize each branch by sampling object instances of proper scales for training. As a bonus, a fast approximation version of TridentNet could achieve significant improvements without any additional parameters and computational cost compared with the vanilla detector. On the COCO dataset, our TridentNet with ResNet-101 backbone achieves state-of-the-art single-model results of 48.4 mAP. Codes are available at https://git.io/fj5vR.

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Yanghao Li, Yuntao Chen, Naiyan Wang, Zhaoxiang Zhang. 2019-08-20. Scale-Aware Trident Networks for Object Detection. https://arxiv.org/abs/1901.01892

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