Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems
Open accessAbstract
To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN). The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI) pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR) sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226 × 370 image, whereas the original selective search method extracted approximately 10 6 × n proposals. We obtained classification performance with 77.72% mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset.
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Processing sparse LiDAR data via clustering or 3D CNN is usually very time-consuming and infeasible for online autonomous driving.
“Processing the sparse data via clustering (e.g. [100], [106]-[108]) or 3D CNN (e.g. [29], [136]) is usually very time-consuming and infeasible for online autonomous driving.”
From Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2021) · cites this paper as [100]
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- Authors
- Oh, S.-I., & Kang, H.-B.
- Affiliations
- Catholic University of Korea (KR)
- Corresponding author
- Kang, Hang-Bong
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2017-01-22
- Volume
- 17
- Issue
- 1
- Pages
- 207
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC5298778
- OpenAlex
- W2580683366
- Cited by
- 120
- References
- 68
- Citations by year
- 2026: 3 · 2025: 11 · 2024: 15 · 2023: 18 · 2022: 15 · 2021: 9 · 2020: 16 · 2019: 16 · 2018: 13 · 2017: 3
- FWCI
- 8.32
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Agency of Defense Development (UD150016ID)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- object detection, object classification, decision fusion, intelligent vehicle systems, LiDAR, 3D point clouds, multi-sensor fusion, object proposal generation, semantic segmentation, KITTI dataset
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