SaccadeDet: A Novel Dual-Stage Architecture for Rapid and Accurate Detection in Gigapixel Images

Abstract

The advancement of deep learning in object detection has predominantly focused on megapixel images, leaving a critical gap in efficient processing of gigapixel images. These super high-resolution images present unique challenges due to their immense size and computational demands. To address this, we introduce ‘SaccadeDet’, an innovative architecture for gigapixel-level object detection, inspired by the human eye saccadic movement. The cornerstone of SaccadeDet is its ability to strategically select and process image regions, dramatically reducing computational load. This is achieved through a two-stage process: the ‘saccade’ stage, which identifies regions of probable interest, and the ‘gaze’ stage, which refines detection in these targeted areas. Our approach, evaluated on the PANDA dataset, not only achieves a 8x speed increase over the state-of-the-art methods but also demonstrates significant potential in gigapixel-level pathology analysis through its application to Whole Slide Imaging.