CSPN: Multi-Scale Cascade Spatial Pyramid Network for Object Detection

摘要

Scale variation is one of the key challenges in object detection. One solution is Image Pyramid, which employs images of multiple resolutions for training. Another solution is Feature Pyramid, which uses multi-scale features for prediction and is widely used in current object detectors due to its high efficiency. However, the representational power of each scale in Feature Pyramid is inconsistent, which makes the performance lower than Image Pyramid. To solve this problem and obtain better detection performance, we propose a novel net-work named Multi-Scale Cascade Spatial Pyramid Network (MS-CSPN) to strengthen Feature Pyramid. First, we de-sign CSPN to expand the receptive field in a cascade form to detect objects of different scales. Secondly, we propose a Cross-Scale Sharing Strategy, which shares the parameters of CSPN at all scales. Finally, we introduce global context information to enhance MS-CSPN. Experimental results on the MS-COCO benchmark show that the proposed MS-CSPN improves the mAP by a large margin compared to previous related works.