Speed up Object Detection on Gigapixel-level Images with Patch Arrangement

With the appearance of super high-resolution (e.g., gigapixel-level) images, performing efficient object detection on such images becomes an important issue. Most ex-isting works for efficient object detection on high-resolution images focus on generating local patches where objects may exist, and then every patch is detected independently. How-ever, when the image resolution reaches gigapixel-level, they will suffer from a huge time cost for detecting numerous patches. Different from them, we devise a novel patch ar-rangement frameworkfor fast object detection on gigapixel-level images. Under this framework, a Patch Arrangement Network (PAN) is proposed to accelerate the detection by determining which patches could be packed together into a compact canvas. Specifically, PAN consists of (1) a Patch Filter Module (PFM) (2) a Patch Packing Module (PPM). PFM filters patch candidates by learning to select patches between two granularities. Subsequently, from the remaining patches, PPM determines how to pack these patches to-gether into a smaller number of canvases. Meanwhile, it generates an ideal layout of patches on canvas. These can-vases are fed to the detector to get final results. Experiments show that our method could improve the inference speed on gigapixel-level images by 5 x while maintaining great performance.