State-of-the-art Models for Object Detection in Various Fields of Application

TL;DR

This paper reviews the latest object detection models and datasets, analyzing their performance and suitability, to guide researchers in achieving state-of-the-art results across various applications.

Contribution

It provides a comprehensive comparison of one-stage and two-stage object detection methods, along with optimal dataset-model combinations based on recent developments.

Findings

Top models identified for each dataset

Optimal dataset-model pairings for improved accuracy

Analysis of inference time and precision across models

Abstract

We present a list of datasets and their best models with the goal of advancing the state-of-the-art in object detection by placing the question of object recognition in the context of the two types of state-of-the-art methods: one-stage methods and two stage-methods. We provided an in-depth statistical analysis of the five top datasets in the light of recent developments in granulated Deep Learning models - COCO minival, COCO test, Pascal VOC 2007, ADE20K, and ImageNet. The datasets are handpicked after closely comparing them with the rest in terms of diversity, quality of data, minimal bias, labeling quality etc. More importantly, our work extends to provide the best combination of these datasets with the emerging models in the last two years. It lists the top models and their optimal use cases for each of the respective datasets. We have provided a comprehensive overview of a variety of both generic and specific object detection models, enlisting comparative results like inference time and average precision of box (AP) fixed at different Intersection Over Union (IoUs) and for different sized objects. The qualitative and quantitative analysis will allow experts to achieve new performance records using the best combination of datasets and models.