Guest Editor(s)
Dr. Youyang Qu
Email: youyang.qu@data61.csiro.au
Affiliation: Commonwealth Scientific and Industrial Research Organisation, Canberra, Australia
Homepage:
Research Interests: edge intelligence, object detection, computer vision, etc.
Dr. Di Wu
Email: d.wu@latrobe.edu.au
Affiliation: Engineering and Mathematical Science, La Trobe University, Melbourne, Australia
Homepage:
Research Interests: federated learning, computer vision, multi-modal LLM
Dr. Jun Bai
Email: jun.bai@mcgill.ca
Affiliation: School of Computer Science, McGill University, Montreal, Canada
Homepage:
Research Interests: distributed compter vsion, AI for health, federated LLM/agent
Summary
Recent advances in artificial intelligence and computer vision have significantly improved the capability of machines to perceive and understand visual information. Among these technologies, object detection has become a fundamental component for numerous applications, including autonomous systems, intelligent surveillance, medical imaging, and industrial inspection. However, real-world visual data are often affected by challenges such as low resolution, noise, occlusion, and complex environments, which may significantly degrade the performance of detection systems.
This Special Issue aims to explore recent advances in object detection and related visual perception techniques in intelligent systems. In addition to core detection algorithms, the issue also welcomes research on image enhancement, super-resolution, restoration, and feature representation methods that can improve the robustness and accuracy of object detection pipelines. The goal is to provide a platform for researchers and practitioners to present innovative methodologies, practical applications, and system-level solutions that advance the reliability and efficiency of visual perception technologies.
Suggested themes include, but are not limited to:
• Advanced object detection algorithms and architectures
• Object detection under challenging conditions (e.g., low resolution, noise, occlusion)
• Image super-resolution and restoration for visual perception systems
• Multi-modal and cross-domain object detection
• Lightweight and edge-based detection models for intelligent systems
• Object detection applications in industrial inspection, smart grid, etc.
Keywords
object detection, visual perception systems, detection under challenging conditions, image super-resolution, image enhancement
Published Papers
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