Deep Learning Techniques for Vehicle Detection and Classification from Images/Videos: A Survey

Area of Science:

• Computer Vision
• Artificial Intelligence
• Intelligent Transportation Systems (ITSs)

Background:

• Vehicle detection and classification from images/videos is crucial for ITSs but challenging due to appearance variations.
• Advancements in Deep Learning (DL) are driving demand for robust computer vision solutions in various fields.

Purpose of the Study:

• To survey and analyze various deep learning approaches for vehicle detection and classification.
• To investigate the applications of these DL techniques in ITSs, including traffic density estimation and toll management.
• To provide a detailed overview of DL techniques, benchmark datasets, and challenges in the field.

Main Methods:

• Comprehensive literature review of deep learning architectures for object detection and classification.
• Analysis of benchmark datasets commonly used for vehicle detection and classification tasks.
• Investigation of performance metrics and challenges associated with real-world ITS applications.

Main Results:

• Deep learning models demonstrate significant improvements in efficiency and robustness for vehicle detection and classification.
• Various DL architectures are effective for diverse ITS applications, from real-time traffic monitoring to automated toll management.
• Key challenges include handling appearance variations, occlusions, and achieving real-time performance.

Conclusions:

• Deep learning is a powerful tool for advancing vehicle detection and classification in intelligent transportation systems.
• Continued research into DL techniques, datasets, and addressing current challenges will further enhance ITS capabilities.
• The paper provides a valuable resource for researchers and practitioners in the field of intelligent transportation and computer vision.