ExYOLO: A small object detector based on YOLOv3 Object Detector

Abstract

Object detection is a hot and popular research area in computer vision. Although object detection algorithm will be greatly speeding up with Deep Neural Networks, like YOLOv3 Object Detector, its performance in accuracy and precision was not satisfactory in small object detection which has less than 1% area in one image. This paper proposes a small object detector, exYOLO, which enhances the shallow feature information by superimposing the 2-fold down-sampled feature map from the original network model. The author attempted the set of experiments with a view to demonstrating accuracy and precision in small object detection, the results of the experiment indicate that exYOLO is a better performance in accuracy and precision.