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Object Detection Based on Lightweight YOLOX for Autonomous Driving
Open accessAbstract
Object detection algorithm is one of the key elements for implementing autonomous driving technology, and the high accuracy and fast inference speed requirements of the algorithm are crucial for safe autonomous driving. In the process of autonomous driving, low accuracy or slow inference speed can both lead to fatal accidents. Therefore, a high-precision and high-efficiency detection algorithm is needed for autonomous driving applications. In this paper, a lightweight feature extraction model, ShuffDet, is proposed to replace the CSPDark53 model used by YOLOX, by improving the YOLOX algorithm. At the same time, an attention mechanism is introduced into the feature pyramid network (FPN) to make the network focus more on important information in the network, thereby improving the accuracy of the model. This model, which combines two methods, is called ShuffYOLOX, and it can improve the accuracy of the model while keeping it lightweight. The performance of the ShuffYOLOX model on the KITTI dataset is tested in this paper, and the experimental results show that compared to the original network, the mean average precision (mAP) of the ShuffYOLOX model on the KITTI dataset reaches 92.20%. In addition, the number of parameters of the ShuffYOLOX model is reduced by 34.57%, the Gflops is reduced by 42.19%, and the FPS is increased by 65%. Therefore, the ShuffYOLOX model is very suitable for autonomous driving applications.
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YOLOX combined ShuffDet backbone and attention mechanism to achieve 92.20% mAP while reducing parameters by 34% on KITTI dataset for road traffic.
“| YOLOX [91] | mAP | Combined ShuffDet backbone and attention mechanism to achieve 92.20% mAP while reducing parameters by 34%. | KITTI dataset for road traffic |”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [91]
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Metadata
- Authors
- He, Q., xu, A., Ye, Z., Zhou, W., & Cai, T.
- Affiliations
- Hubei University of Technology (CN)
- Hubei University of Technology
- Corresponding author
- Ye, Zhiwei
- Venue
- Sensors
- Publisher
- Research Square Platform LLC
- Published
- 2023-06-16
- Volume
- 23
- Issue
- 17
- Pages
- 7596-7596
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC10490816
- OpenAlex
- W4386380484
- Cited by
- 53
- References
- 34
- Citations by year
- 2026: 6 · 2025: 23 · 2024: 19 · 2023: 5
- FWCI
- 4.81
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Sensors (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (42201464)
- 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, YOLOX, lightweight neural networks, autonomous driving, attention mechanism, driver assistance systems, KITTI dataset, real-time inference
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