Authors: Abdul Hannan Khan, Syed Tahseen Raza Rizvi, Andreas Dengel
Published on: January 31, 2024
Impact Score: 7.6
Arxiv code: Arxiv:2402.00128
Summary
- What is new: Extension of the LSFM model for general object detection and real-time application in traffic scenes, including a proposal for a more suitable performance indicator for autonomous driving.
- Why this is important: Modern computer vision techniques prioritize accuracy over efficiency, leading to high computational demands and inefficiencies in real-time applications like autonomous driving.
- What the research proposes: Use of the previously proposed LSFM (Lightweight and Speedy Feature Matcher) model for efficient and real-time pedestrian and object detection in autonomous driving, under various conditions.
- Results: The extended LSFM model demonstrated superior performance in real-time object detection on traffic scenes, showing low latency and high generalizability, with improved suitability for autonomous driving applications.
Technical Details
Technological frameworks used: LSFM (Lightweight and Speedy Feature Matcher)
Models used: Extended LSFM model for general object detection
Data used: Autonomous driving benchmarks, traffic object detection datasets
Potential Impact
Automotive companies, autonomous vehicle startups, onboard hardware manufacturers for autonomous driving technologies, and AI-driven traffic management solutions.
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