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YOLOdrive: A Lightweight Autonomous Driving Single-Stage Target Detection Approach
Abstract
With the continuous development of autonomous driving, real-time target detection has become increasingly critical in autonomous driving systems. However, traditional target detection algorithms usually require huge computational resources, limiting their application on embedded autonomous driving platforms. To address this challenge, a lightweight single-stage target detection algorithm is proposed YOLOdrive. The inverted residual structure, linear bottleneck layer, and depth-separable convolution in MobileNetv2 are utilized to improve the YOLOv8 backbone network, while the spatial channel reconstructed convolution is used to improve the C2f module of YOLOv8, and the convolution of YOLOv8 neck and detection head is replaced by the depth-separable convolution. Experimental results verify that the average accuracy of YOLOdrive algorithm on MS COCO2017 data set and VOC2007 data set is improved compared with the baseline model YOLOv8-Nano. The amount of model parameters has been reduced by more than 50%, and the amount of computation in the model has been reduced by more than 70%. The algorithm drastically reduces the amount of parameters and computational complexity of the network, improves the operational efficiency, saves the storage space of the network, and maintains a high-detection performance.
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“Single-stage detectors find applications in various domains, including autonomous vehicles, surveillance, robotics, and object recognition in images and videos. Their speed and simplicity make them particularly well-suited for scenarios where real-time detection is crucial. These applications are supported by numerous studies that highlight the efficiency and effectiveness of single-stage detec”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [25]
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Metadata
- Authors
- Wang, L., Hua, S., Zhang, C., Yang, G., Ren, J., & Li, J.
- Affiliations
- North China University of Science and Technology (CN)
- Venue
- IEEE Internet of Things Journal
- Published
- 2024-11-15
- Volume
- 11
- Issue
- 22
- Pages
- 36099-36113
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W4401387091
- Cited by
- 14
- References
- 32
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 1
- FWCI
- 1.97
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Internet of Things Journal (publishedVersion)
- Funders
- Basic Scientific Research Business Expenses of Hebei Provincial Universities (JST2022001)
- Tangshan Science and Technology Project (22130225G)
- Tangshan Science and Technology Bureau (21130211D)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- autonomous driving, object detection, YOLOv8, lightweight neural networks, MobileNetV2, depthwise separable convolution, embedded systems, real-time detection, model compression
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