Research on Intelligent Video Detection of Small Targets Based on Deep Learning Intelligent Algorithm.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Machine Learning

Background:

• Traditional object detection algorithms struggle with complex scenarios, motivating research into deep learning approaches.
• Deep learning object detection is categorized into two-stage and single-stage algorithms, each with distinct advantages and limitations.

Purpose of the Study:

• To provide a comprehensive overview of deep learning-based object detection, with a focus on small object detection.
• To summarize common datasets, performance metrics, and challenges in object detection.
• To review recent advancements and future prospects in deep learning for object detection.

Main Methods:

• Systematic review of classical and deep learning-based object detection algorithms.
• Analysis of various datasets, including their characteristics and detection difficulties.
• Exploration of multiscale and super-resolution techniques for small object detection.

Main Results:

• Deep learning methods demonstrate enhanced robustness compared to traditional algorithms.
• Key challenges in small object detection are identified, alongside emerging solutions.
• Lightweight strategies and their impact on model performance are discussed.

Conclusions:

• Deep learning-based object detection shows significant promise, especially for challenging tasks like small object detection.
• Further research is needed to address limitations and optimize performance in real-world applications.
• Future directions include advancements in multiscale processing, super-resolution, and model efficiency.