Lightdet: A Lightweight And Accurate Object Detection Network
Qiankun Tang, Jie Li, Yu Hu, Zhiping Shi
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The extensive computational burden limits the usage of accurate but complex object detectors in resource-bounded scenarios. In this paper, we present a lightweight object detector, named LightDet, to address this dilemma. We design a lightweight backbone that is able to capture rich low-level features by the proposed Detail-Preserving Module. To effectively aggregate bottom and top-down features, we introduce an efficient Feature-Preserving and Refinement Module. A lightweight prediction head is employed to further reduce the entire network complexity. Experimental results show that our LightDet achieves 75.5\% mAP on PASCAL VOC 2007 at the speed of 250 FPS and 24.0\% mAP on MS COCO dataset.