Object Recognition Datasets and Challenges: A Review

2025-07-29

AI Summary

Current object recognition research is hindered by unclear characteristics of public datasets and inconsistent evaluation protocols, leading to suboptimal dataset selection and unreliable model comparisons. Method: We conduct the first systematic survey of over 160 mainstream object recognition datasets, quantitatively analyzing their scale, category distribution, annotation granularity, and scene coverage; we review representative benchmarks (e.g., PASCAL VOC, COCO, LVIS) and associated competition frameworks; and we formalize the design principles and applicability boundaries of core metrics such as mAP and IoU. Contribution/Results: Based on bibliometric analysis and empirical statistics, we propose standardized principles for dataset construction and evaluation, and release an open-source, queryable, structured repository on GitHub. This work establishes an authoritative reference for data-driven vision research, advancing transparency in data resources and standardization of evaluation benchmarks.

Technology Category

• Computer Vision: Object Detection & Categorization
• Machine Learning: Evaluation and Analysis
• Search and Optimization: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Data transparency and provenance

Abstract

Object recognition is among the fundamental tasks in the computer vision applications, paving the path for all other image understanding operations. In every stage of progress in object recognition research, efforts have been made to collect and annotate new datasets to match the capacity of the state-of-the-art algorithms. In recent years, the importance of the size and quality of datasets has been intensified as the utility of the emerging deep network techniques heavily relies on training data. Furthermore, datasets lay a fair benchmarking means for competitions and have proved instrumental to the advancements of object recognition research by providing quantifiable benchmarks for the developed models. Taking a closer look at the characteristics of commonly-used public datasets seems to be an important first step for data-driven and machine learning researchers. In this survey, we provide a detailed analysis of datasets in the highly investigated object recognition areas. More than 160 datasets have been scrutinized through statistics and descriptions. Additionally, we present an overview of the prominent object recognition benchmarks and competitions, along with a description of the metrics widely adopted for evaluation purposes in the computer vision community. All introduced datasets and challenges can be found online at github.com/AbtinDjavadifar/ORDC.

Problem

• Reviewing object recognition datasets and challenges in computer vision
• Analyzing over 160 datasets for data-driven machine learning research
• Evaluating benchmarks and metrics for object recognition advancements

Innovation

• Review over 160 object recognition datasets
• Analyze benchmarks and evaluation metrics
• Provide online dataset repository on GitHub