Object Detection on Road: Vehicle's Detection Based on Re-Training Models on NVIDIA-Jetson Platform
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Summary
This summary is machine-generated.For congested urban traffic, MobileNetV1-SSD offers the best balance of accuracy and computational efficiency for advanced driver-assistance systems (ADAS) on embedded devices. This artificial intelligence (AI) approach improves vehicle detection, even for smaller classes like motorcycles.
Area Of Science
- Computer Vision and Artificial Intelligence
- Embedded Systems Engineering
- Transportation and Traffic Management
Background
- Growing use of AI and deep learning (DL) for vehicle classification and detection on embedded devices.
- Deployment constraints on embedded systems include computational cost and response time, especially in congested urban areas.
- Need to balance model accuracy, embedded system type, and dataset characteristics for urban traffic analysis.
Purpose Of The Study
- To evaluate the trade-off between model accuracy and computational load for vehicle detection in congested urban environments.
- To identify the optimal SSD-based deep learning model for implementation in ADAS embedded systems.
- To assess the impact of data augmentation on detecting minority vehicle classes.
Main Methods
- Acquisition and manual labeling of urban traffic videos from Lima, Peru.
- Training of three Single Shot Detector (SSD)-based models (MobileNetV1-SSD, MobileNetV2-SSD-Lite, VGG16-SSD) on an NVIDIA Jetson Orin NX platform.
- Utilized a methodology adapted from the CRISP-DM approach, including data augmentation via contrast adjustment.
Main Results
- VGG16-SSD achieved the highest mean average precision (mAP ≈90.7%) but required longer training.
- MobileNetV1-SSD (512×512) demonstrated comparable performance (mAP ≈90.4%) with significantly reduced training time.
- Data augmentation improved detection rates for minority classes like Tuk-tuks and motorcycles.
Conclusions
- MobileNetV1-SSD (512×512) offers the best compromise between accuracy and computational efficiency for ADAS in congested urban settings.
- The study highlights the importance of selecting appropriate AI models based on specific deployment constraints and traffic scenarios.
- Contrast adjustment data augmentation is effective for enhancing the detection of underrepresented vehicle types.