'Skimming-Perusal' Detection: A Simple Object Detection Baseline in GigaPixel-level Images

Zhibin Zhang, Wanli Xue, Kaihua Zhang, Shengyong Chen

Abstract: Object detection has achieved amazing performance in regular-sized images, but with the emergence of gigapixel-level images, even the most advanced object detection methods cannot be directly used to process them quickly and efficiently. Therefore, this paper proposes a simple baseline for gigapixel-level images object detection called Skimming-Perusal Detection (SPDet). The SPDet consists mainly of two parts, a skimming model and a perusal model. The skimming model is based on an efficient global-to-local search strategy to detect possible regions containing objects. Non-object regions are merged through a skimming iterative merging strategy to generate skimming patch candidates. The perusal model adaptive scales the skimming patch candidates guided by the coarse detection of the skimming model. Extensive evaluations on the PANDA dataset demonstrate that the SPDet boosts detection speed on gigapixel-level images by 6× while achieving better performance than a variety of state-of-the-art methods. The source code is released at https://github.com/TJUT-CV/SPDet.