Abstract & Overview
• Small object detection (SOD) is a critical yet challenging task in computer vision. • This survey provides a review of articles published in Q1 journals during 2024–25. • We analyzed challenges, techniques, datasets, metrics, and real-world applications. 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. For example, several methods highlight the practical impact of architectural advances in SOD. FFCA-YOLO achieves APs of 0.748/0.617/0.909 on VEDAI/AI-TOD/USOD in remote sensing, and DsP-YOLO targets industrial defect detection, reaching 95.8% mAP on PCB-DET. 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. By consolidating recent findings and identifying research gaps, this survey serves as a valuable resource for researchers aiming to advance SOD methodologies.