A systematic literature review on deep learning approaches for small object detection

Object detection is essential in several industries, including defense, autonomous vehicles, and surveillance. These applications rely on various devices equipped with cameras, such as vehicles, drones, and satellites; primarily operating in the visible spectral domain rather than infrared or other spectral ranges. Deep Learning (DL) techniques have significantly advanced the field of object detection, enabling the identification of various objects. However, detecting tiny objects remains a challenging task. Despite its difficulty, identifying small objects in images captured by these devices in the visible spectrum is crucial. It is essential to explore hybrid techniques and modifications in feature architectures to address the challenge of detecting tiny objects. Simple architectures often fall short in this regard, necessitating more sophisticated approaches. This paper systematically reviews different DL-based approaches researchers have previously employed to tackle this issue. A systematic literature review on SOD and DL techniques uses the ”Preferred Reporting Items for Systematic Reviews and Meta-Analysis” (PRISMA) methodology. It discusses various DL-based theoretical frameworks, including Reinforcement Learning and Generative Adversarial Networks, specifically for Small Object Detection (SOD) in visible spectral images. The review begins by defining a small object and identifying the datasets available for various applications, such as remote sensing and autonomous vehicles. It then examines the implementation of models according to these datasets and analyzes the findings from other researchers. The analysis reveals that, for most datasets, the average precision (AP) for SOD ranges from 20% to 40% and showcases the need for the advancement and focus.