Object Detection for Autonomous Driving using YOLO [You Only Look Once] algorithm
Abstract
The field of autonomous driving is going to be the face of the automobile industry very soon. The number of accidents that take place because of human error currently is very high and it can be slashed to a huge extent with the advent of autonomous driving. One of the primary prerequisites and a huge part of autonomous driving is dependent on object detection through computer vision, this paper aims at aiding towards the field of autonomous driving by helping detect objects with the use of deep learning algorithms. Research work used state-of-the-art algorithm YOLO (you only look once) to detect different objects that appear on the road and classified into the category that they belong to with the help of bounding boxes. The weights of the YOLO v4 is utilized to custom train our model to detect the objects and the data will be collected from the open images dataset using its OIDv4 toolkit.
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YOLOv4 was trained on a custom dataset for object detection, achieving an mAP of 74.6%, F1-score of 0.70, and recall of 0.81.
“| YOLOv4 [92] | mAP, F1-score, Recall | YOLOv4 was trained on a custom dataset for object detection, achieving an mAP of 74.6%, F1-score of 0.70, and recall of 0.81. | Custom vehicle dataset |”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [92]
Cites
Deep Residual Learning for Image Recognition
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 56,035 · see citing works
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803 · see citing works
Feature Pyramid Networks for Object Detection
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Soft-NMS — Improving Object Detection with One Line of Code
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DetNAS: Backbone Search for Object Detection
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Flagged first, then by citations
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Ali, M. L., & Zhang, Z. (2024) · Computers · doi:10.3390/computers13120336 · Cited by 383 · see citing works
- Authors
- Sarda, A., Dixit, S., & Bhan, A.
- Affiliations
- Amity University (IN)
- Venue
- 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV)
- Publisher
- IEEE
- Published
- 2021-02-04
- Pages
- 1370-1374
- OpenAlex
- W3147093102
- Cited by
- 77
- References
- 7
- Citations by year
- 2026: 4 · 2025: 19 · 2024: 24 · 2023: 15 · 2022: 12 · 2021: 3
- FWCI
- 4.42
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- object detection, autonomous driving, YOLOv4, bounding boxes
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