The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
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]
Citations
Cites
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Yurtsever, E., Lambert, J., Carballo, A., & Takeda, K. (2020) · IEEE Access · doi:10.1109/access.2020.2983149 · Cited by 1,889
An End-to-End Steel Surface Defect Detection Approach via Fusing Multiple Hierarchical Features
He, Y., Song, K., Meng, Q., & Yan, Y. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2915404 · Cited by 1,341 · cited 1×
“In [9], [10], and [11], convolutional neural networks are utilized to automatically learn the feature representation of an image and combined with methods, such as region proposal networks or anchor frames, to generate candidate regions, which are then classified and regressed to obtain the final…”
MethodFoveaBox: Beyound Anchor-Based Object Detection
Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., & Shi, J. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3002345 · Cited by 1,142
Autonomous Vehicles That Interact With Pedestrians: A Survey of Theory and Practice
Rasouli, A., & Tsotsos, J. K. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2901817 · Cited by 884
SCConv: Spatial and Channel Reconstruction Convolution for Feature Redundancy
Li, J., Wen, Y., & He, L. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00596 · Cited by 856
Self-citationRIFT: Multi-Modal Image Matching Based on Radiation-Variation Insensitive Feature Transform
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Self-citationAnomaly Detection Based on Convolutional Recurrent Autoencoder for IoT Time Series
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MobileNetV2 Model for Image Classification
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Cited by
Sorted by citations
Ali, M. L., & Zhang, Z. (2024) · Computers · doi:10.3390/computers13120336 · Cited by 383
An Improved YOLOv8-Based Lightweight Attention Mechanism for Cross-Scale Feature Fusion
Liu, S., Shao, F., Chu, W., Dai, J., & Zhang, H. (2025) · Remote Sensing · doi:10.3390/rs17061044 · Cited by 38
YOLOv8-SCS: Improved Object Detection for Autonomous Driving Under Adverse Weather Conditions
Lee, Y., & Kang, J. (2025) · IEEE Access · doi:10.1109/access.2025.3602211 · Cited by 12
Ma, Q., Fan, X., Chen, H., & Li, T. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07057-5 · Cited by 8
MSVDNet: A multi-scale vehicle detection network for target detection in autonomous driving
Li, B., Hu, X., Zhang, L., Li, Q., & Hu, J. (2025) · Alexandria Engineering Journal · doi:10.1016/j.aej.2025.08.025 · Cited by 5
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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- Wang, L., Hua, S., Zhang, C., Yang, G., Ren, J., & Li, J. (2024). YOLOdrive: A Lightweight Autonomous Driving Single-Stage Target Detection Approach. IEEE Internet of Things Journal. https://doi.org/10.1109/jiot.2024.3439863
