Evaluation of real-time object detection model based on small targets

Abstract

In recent years, with the applications of object detection increasingly extensive, the approaches based on Deep Learning have achieved state-of-the-art performance on challenging datasets. Some researchers have made demands on real-time performance while paying attention to the accuracy of the model. In addition, with the rapid development of the object detection model, the detection of small targets has attracted extensive attention. Although several evaluations of the models have been conducted, we have conducted a more detailed evaluation of the small targets real-time detection. In this work, we carried out an in-depth evaluation of the latest real-time object detection model. We evaluate three state-of-the- art models including Single Shot MultiBox Detector (SSD), You Only Look Once version 2 (YOLO v2), and You Only Look Once version 3 (YOLO v3) with related trade-off factors i.e. accuracy, execution time and resource usage. Experiments were conducted on benchmark datasets and a newly generated dataset for small object detection. All analyses and findings are then presented.