Deep learning-based small object detection: A survey.

Area of Science:

• Computer Vision
• Artificial Intelligence

Background:

• Small object detection (SOD) is crucial for applications like autonomous driving and remote sensing.
• SOD is challenging due to low resolution and noise, but deep learning offers performance improvements.

Purpose of the Study:

• To analyze deep learning-based SOD research from four key perspectives.
• To review literature on critical SOD tasks like small face, pedestrian, and aerial object detection.
• To evaluate the performance of SOD algorithms on benchmark datasets.

Main Methods:

• Analyzing deep learning papers focusing on boosting input feature resolution, scale-aware training, contextual information, and data augmentation.
• Conducting a comprehensive performance evaluation of SOD algorithms on four standard datasets.
• Reviewing literature specific to small face, pedestrian, and aerial image object detection.

Main Results:

• Network configurations that boost input feature resolution yield significant performance gains.
• Experimental results demonstrate the effectiveness of analyzed methods on WIDER FACE and Tiny Person datasets.
• Identified key strategies for improving SOD accuracy.

Conclusions:

• Boosting input feature resolution is a highly effective strategy for enhancing small object detection.
• Further research directions in SOD are identified based on current challenges and findings.
• Deep learning approaches show great promise for advancing SOD capabilities.