{"title":"Comprehensive review of recent developments in visual object detection based on deep learning","authors":"Enerst Edozie, Aliyu Nuhu Shuaibu, Ukagwu Kelechi John, Bashir Olaniyi Sadiq","doi":"10.1007/s10462-025-11284-w","DOIUrl":null,"url":null,"abstract":"<div><p>This comprehensive review looks into the recent developments in visual object detection, focusing on the transformative effect of deep learning (DL) technologies. In object detection, computer vision is a basic issue. This involves object detection and location in the video and image frames, which has notable advantages in robotics, autonomous driving, medical imaging, and surveillance. This review, therefore, presents a thorough integration analysis in visual object detection of the latest developments, providing both the historical context and state-of-the-art analysis. This review categorizes current methods into one-stage and two-stage frameworks, studying their architectural innovations, detection accuracy, computational speed, and deployment readiness. This review further scrutinizes the performance measures, emphasizes the inevitability of large-scale annotated datasets, and provides a curated overview of the widely used datasets in the field. Notable features include a discussion of practical applications and current research trends, and a comprehensive comparative analysis that compares models based on accuracy, speed, and trade-offs. A unique addition of this work is a thorough comparative analysis table that benchmarks traditional and modern models in terms of mean Average Precision (mAP), frames per second (FPS), advantages, limitations, and the coverage of transformer-based models and real-time deployments. The review’s holistic approach provides significant insights for researchers and practitioners seeking to understand, benchmark, develop, or benchmark object detection systems.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 9","pages":""},"PeriodicalIF":13.9000,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11284-w.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial Intelligence Review","FirstCategoryId":"94","ListUrlMain":"https://link.springer.com/article/10.1007/s10462-025-11284-w","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
This comprehensive review looks into the recent developments in visual object detection, focusing on the transformative effect of deep learning (DL) technologies. In object detection, computer vision is a basic issue. This involves object detection and location in the video and image frames, which has notable advantages in robotics, autonomous driving, medical imaging, and surveillance. This review, therefore, presents a thorough integration analysis in visual object detection of the latest developments, providing both the historical context and state-of-the-art analysis. This review categorizes current methods into one-stage and two-stage frameworks, studying their architectural innovations, detection accuracy, computational speed, and deployment readiness. This review further scrutinizes the performance measures, emphasizes the inevitability of large-scale annotated datasets, and provides a curated overview of the widely used datasets in the field. Notable features include a discussion of practical applications and current research trends, and a comprehensive comparative analysis that compares models based on accuracy, speed, and trade-offs. A unique addition of this work is a thorough comparative analysis table that benchmarks traditional and modern models in terms of mean Average Precision (mAP), frames per second (FPS), advantages, limitations, and the coverage of transformer-based models and real-time deployments. The review’s holistic approach provides significant insights for researchers and practitioners seeking to understand, benchmark, develop, or benchmark object detection systems.
期刊介绍:
Artificial Intelligence Review, a fully open access journal, publishes cutting-edge research in artificial intelligence and cognitive science. It features critical evaluations of applications, techniques, and algorithms, providing a platform for both researchers and application developers. The journal includes refereed survey and tutorial articles, along with reviews and commentary on significant developments in the field.