Enhanced Object Detection with Deep Convolutional Neural Networks for Advanced Driving Assistance
- Submitting institution
-
Aston University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 38248842
- Type
- D - Journal article
- DOI
-
10.1109/TITS.2019.2910643
- Title of journal
- IEEE Transactions on Intelligent Transportation Systems
- Article number
- 8694965
- First page
- 1572
- Volume
- 21
- Issue
- 4
- ISSN
- 1524-9050
- Open access status
- Compliant
- Month of publication
- April
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
5
- Research group(s)
-
B - Aston Institute of Urban Technology and the Environment (ASTUTE)
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper proposed an enhanced object detection approach for advanced driving assistance systems (ADAS). The work is significant because it largely improves the detection accuracy and speed with enhancements to tackle the object scale variation and occlusion challenges. The proposed approach was ranked top on car and pedestrian detection among the published works in the leader board of KITTI benchmark for ADAS. The work led to an H2020 research grant (£1.2 million, COSAFE#824092) and invited paper (DOI: 10.1109/ICNP.2019.8888126). It was widely followed by national and international groups (including DOI: 10.1016/j.patcog.2019.107182, DOI:10.1109/ACCESS.2019.2929432, DOI: 10.1109/TITS.2019.2961145).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
