An Effective Approach of Vehicle Detection Using Deep Learning

Area of Science:

• Computer Vision
• Artificial Intelligence
• Intelligent Transportation Systems

Background:

• Vehicle detection is crucial for autonomous driving and intelligent transportation.
• Deep learning methods have significantly advanced object detection capabilities.

Purpose of the Study:

• To investigate and compare the performance of YOLOv3 and SSD deep learning algorithms for vehicle detection.
• To evaluate the effectiveness of these algorithms on an open-source road vehicle dataset.

Main Methods:

• Utilized deep learning techniques for vehicle detection.
• Trained and compared two primary object detection algorithms: YOLOv3 and SSD.
• Processed and trained on an open-source road vehicle dataset.

Main Results:

• Comparative analysis of vehicle detection effects between YOLOv3 and SSD models.
• Summarized characteristics of the trained models.
• Identified performance differences between the two algorithms.

Conclusions:

• The study provides insights into the comparative performance of YOLOv3 and SSD for vehicle detection.
• Findings can inform the selection of appropriate algorithms for target tracking, semantic segmentation, and autonomous driving applications.