  Detection of small objects and objects far away in the scene is a major
challenge in surveillance applications. Such objects are represented by small
number of pixels in the image and lack sufficient details, making them
difficult to detect using conventional detectors. In this work, an open-source
framework called Slicing Aided Hyper Inference (SAHI) is proposed that provides
a generic slicing aided inference and fine-tuning pipeline for small object
detection. The proposed technique is generic in the sense that it can be
applied on top of any available object detector without any fine-tuning.
Experimental evaluations, using object detection baselines on the Visdrone and
xView aerial object detection datasets show that the proposed inference method
can increase object detection AP by 6.8%, 5.1% and 5.3% for FCOS, VFNet and
TOOD detectors, respectively. Moreover, the detection accuracy can be further
increased with a slicing aided fine-tuning, resulting in a cumulative increase
of 12.7%, 13.4% and 14.5% AP in the same order. Proposed technique has been
integrated with Detectron2, MMDetection and YOLOv5 models and it is publicly
available at https://github.com/obss/sahi.git .
