A Review of Intelligent Driving Pedestrian Detection Based on Deep Learning.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Machine Learning

Background:

• Pedestrian detection, a specialized object detection task, is crucial for intelligent driving and security monitoring.
• Deep learning advancements have significantly improved pedestrian detection, yet performance gaps with human perception persist.
• Current methods face challenges in real-time processing and model efficiency for intelligent driving applications.

Purpose of the Study:

• To review the evolution of pedestrian detection technology.
• To summarize deep learning-based approaches in pedestrian detection.
• To analyze current challenges and future research directions.

Main Methods:

• Review of historical development in pedestrian detection.
• Summary of deep learning techniques applied to pedestrian detection.
• Analysis of pedestrian detection datasets and evaluation metrics.

Main Results:

• Deep learning has driven substantial progress in pedestrian detection accuracy.
• Key issues include real-time performance, model size reduction, and closing the perception gap.
• Existing datasets and evaluation criteria are vital for benchmarking and development.

Conclusions:

• Pedestrian detection technology has matured significantly due to deep learning.
• Further research is needed to enhance real-time capabilities, model efficiency, and robustness.
• Future directions may involve novel architectures and training strategies to overcome current limitations.