Small Object Detection: A Comprehensive Survey on Challenges, Techniques and Real-World Applications
Journal:
arXiv
Published Date:
Mar 26, 2025
Abstract
Small object detection (SOD) is a critical yet challenging task in computer
vision, with applications like spanning surveillance, autonomous systems,
medical imaging, and remote sensing. Unlike larger objects, small objects
contain limited spatial and contextual information, making accurate detection
difficult. Challenges such as low resolution, occlusion, background
interference, and class imbalance further complicate the problem. This survey
provides a comprehensive review of recent advancements in SOD using deep
learning, focusing on articles published in Q1 journals during 2024-2025. We
analyzed challenges, state-of-the-art techniques, datasets, evaluation metrics,
and real-world applications. Recent advancements in deep learning have
introduced innovative solutions, including multi-scale feature extraction,
Super-Resolution (SR) techniques, attention mechanisms, and transformer-based
architectures. Additionally, improvements in data augmentation, synthetic data
generation, and transfer learning have addressed data scarcity and domain
adaptation issues. Furthermore, emerging trends such as lightweight neural
networks, knowledge distillation (KD), and self-supervised learning offer
promising directions for improving detection efficiency, particularly in
resource-constrained environments like Unmanned Aerial Vehicles (UAV)-based
surveillance and edge computing. We also review widely used datasets, along
with standard evaluation metrics such as mean Average Precision (mAP) and
size-specific AP scores. The survey highlights real-world applications,
including traffic monitoring, maritime surveillance, industrial defect
detection, and precision agriculture. Finally, we discuss open research
challenges and future directions, emphasizing the need for robust domain
adaptation techniques, better feature fusion strategies, and real-time
performance optimization.
