LADet: A Light-Weight and Adaptive Network for Multi-Scale Object Detection

Abstract

Scale variation is one of the most significant challenges for object detection task. In comparison with previous one-stage object detectors that simply make feature pyramid network deeper without consideration of speed, we propose a novel one-stage object detector called LADet, which consists of two parts, Adaptive Feature Pyramid Module(AFPM) and Light-weight Classification Function Module(LCFM). Adaptive Feature Pyramid Module generates complementary semantic information for each level feature map by jointly utilizing multi-level feature maps from backbone network, which is different from the top-down manner. Light-weight Classification Function Module is able to exploit more type of anchor boxes without a dramatic increase of parameters because of the utilization of interleaved group convolution. Extensive experiments on PASCAL VOC and MS COCO benchmark demonstrate that our model achieves a better trade-off between accuracy and efficiency over the comparable state-of-the-art detection methods.