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Journal Article
EMOS: Enhanced Moving Object Detection and Classification via Sensor Fusion and Noise Filtering
- Authors
- Dongjin Lee, Seung-Jun Han, Kyoung-Wook Min, Jungdan Choi, Cheong Hee Park
- Issue Date
- 2023-10
- Citation
- ETRI Journal, v.45, no.5, pp.847-861
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원
- Language
- English
- Type
- Journal Article
- Abstract
- Dynamic object detection is essential for ensuring safe and reliable autonomous driving. Recently, light detection and ranging (LiDAR)‐based object detection has been introduced and shown excellent performance on various benchmarks. Although LiDAR sensors have excellent accuracy in estimating distance, they lack texture or color information and have a lower resolution than conventional cameras. In addition, performance degradation occurs when a LiDAR‐based object detection model is applied to different driving environments or when sensors from different LiDAR manufacturers are utilized owing to the domain gap phenomenon. To address these issues, a sensor‐fusion‐based object detection and classification method is proposed. The proposed method operates in real time, making it suitable for integration into autonomous vehicles. It performs well on our custom dataset and on publicly available datasets, demonstrating its effectiveness in real‐world road environments. In addition, we will make available a novel three‐dimensional moving object detection dataset called ETRI 3D MOD.
- KSP Keywords
- Autonomous vehicle, Classification method, Color information, Detection model, Dynamic Object Detection, LiDAR sensors, Light detection and Ranging(LiDAR), Moving Object Detection, Noise filtering, Real-Time, autonomous driving
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
