Collection Overview

Scientific Reports has launched a Guest-Edited Collection on Deep learning for real-time object detection.

Real-time object detection is a critical capability in computer vision, enabling systems to identify and localize objects instantly in dynamic environments. Recent advances leverage optimized convolutional neural networks (e.g., YOLO, SSD) and transformer-based models to achieve low-latency and high-throughput performance on edge- and embedded-devices. 

This will be a Collection of original research papers and will be open for submissions from all authors – on the condition that the manuscripts fall within the scope of the Collection and of Scientific Reports more generally. We are welcoming submissions until 17th April 2026.

Why is this Collection important? 

"Real-time object detection is an important task in computer vision and has wide real-world applications from autonomous driving, robotics, to surveillance and so on. The research on real-time object detection pushes the boundaries of accuracy, speed on practical object detection system. By bringing together innovations across model design, optimization, and deployment, this collection can drive the field forward." - Dr. Lei Zhu

"This topic is important because the real-time object detection has a wide range of applications like self-driving cars, assistive tools, and security systems, where fast decisions can improve safety and efficiency.

I’m excited because it brings together new ideas that make object detection faster and more accurate. It’s a chance to explore how deep learning can be used in many practical situations.

This Collection can inspire better and more efficient models that work well in real time. This could guide future research and help bring advanced AI solutions into everyday use. 

Researchers can share innovative work with a wide audience interested in practical AI. Scientific Reports is a great platform to showcase contributions and connect with others working in this fast growing area." - Dr. Amrit Pal

This Collection supports and amplifies research related to SDG9 Industry, Innovation & Infrastructure

Why submit to a Collection? 

Collections like this one help promote high-quality science. They are led by Guest Editors, who are experts in their fields, and In-House Editors and are supported by a dedicated team of Commissioning Editors and Managing Editors at Springer Nature. Collection manuscripts typically see higher citations, downloads, and Altmetric scores, and provide a one-stop-shop on a cutting-edge topic of interest. 

Who is involved? 

Guest Editors:

• Abhishek Gupta, CSIR-Central Scientific Instruments Organisation, India
• Kwonmoo Lee, Boston Children’s Hospital, Harvard Medical School, USA
• Amrit Pal, Vellore Institute of Technology Chennai, India
• Lei Zhu, Institute of High Performance Computing (IHPC), A*STAR, Singapore

Internal Team:

• In-House Editor: Dr. Thomas Tischer, Scientific Reports, Germany
• Commissioning Editor: Faija Miah, Fully OA Brands, Springer Nature, UK
• Managing Editor: Chantale Davies, Fully OA Brands, Springer Nature, UK

How can I submit my paper? 

Visit the Collection page to find out more about this Collection and how to submit your article.