| Wu Jin (吴 进),Xie Xingjun,Luo Junhang,Shi Yaohao.[J].高技术通讯(英文),2026,32(3):270~281 |
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| Research on autonomous driving object detection based on CM-YOLOv8 |
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| DOI：10. 3772 / j. issn. 1006-6748. 2026. 03. 006 |
| 中文关键词: |
| 英文关键词: autonomous driving, object detection, lightweight, multi-scale fusion, spatial position encoding |
| 基金项目: |
| Author Name | Affiliation | | Wu Jin (吴 进) | (School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, P. R. China) | | Xie Xingjun | | | Luo Junhang | | | Shi Yaohao | |
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| 中文摘要: |
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| 英文摘要: |
| To tackle the dual challenges of low accuracy in small object detection and high computational
complexity in current autonomous driving algorithms , this paper proposes an enhanced object detec-
tion framework based on the you only look once version 8 (YOLOv8) . First , the original C2f module
is substituted with a lightweight GhostBottleneck to minimize computational overhead. Second ,a
multi-scale fusion mechanism is integrated into the neck network to optimize the aggregation of deep
and shallow features. Finally , addressing the insufficient utilization of spatial information in standard
fusion , we introduce spatial position encoding to perform weighted feature fusion. This strategy sim-
ultaneously compresses features to reduce computational load and boosts prediction accuracy. Exper-
imental results on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI)
dataset demonstrate that the proposed model achieves a precision of 92. 4% , mAP@ 50 of 91. 9% ,
and mAP@ 50∶ 95 of 73. 9% . Compared to the baseline YOLOv8 , these metrics represent improve-
ments of 3. 2% , 7. 4% , and 15. 7% , respectively. Furthermore , the model outperforms main-
stream detectors such as YOLOv5 , YOLOv11 , and real-time detection Transformer ( RT-DETR-L) ,
successfully balancing a lightweight design with superior detection performance for autonomous driv-
ing scenarios. |
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