EVALUATING SAMPLING-BASED TECHNIQUE FOR IMPROVED SMALL OBJECT DETECTION USING APPROPRIATE SIZES OF THE ANCHOR BOXES

Abstract

Small object detection is a challenging task in computer vision due to the limited spatial information and low resolution of these objects. Existing generic object detectors demonstrate satisfactory performance on medium and large-sized objects, but they often struggle to accurately recognize small objects. The challenges arise from the low resolution and simple shape characteristics typically associated with small objects. In this paper, we propose an up sampling-based method for end-to-end tiny object identification that outperforms the existing state-of-the-art methods. We, like other contemporary approaches, first produce suggestions before labeling them. In the event of somewhat little things, we recommend tweaks to both of these procedures.