LEAD-YOLO: A Lightweight and Accurate Network for Small Object Detection in Autonomous Driving
The accurate detection of small objects remains a critical challenge in autonomous driving systems, where improving detection performance typically comes at the cost of increased model complexity, conflicting with the lightweight requirements of edge deployment. To address this dilemma, this paper p...
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MDPI AG
2025-08-01
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| author | Yunchuan Yang Shubin Yang Qiqing Chan |
| author_facet | Yunchuan Yang Shubin Yang Qiqing Chan |
| author_sort | Yunchuan Yang |
| collection | DOAJ |
| description | The accurate detection of small objects remains a critical challenge in autonomous driving systems, where improving detection performance typically comes at the cost of increased model complexity, conflicting with the lightweight requirements of edge deployment. To address this dilemma, this paper proposes LEAD-YOLO (Lightweight Efficient Autonomous Driving YOLO), an enhanced network architecture based on YOLOv11n that achieves superior small object detection while maintaining computational efficiency. The proposed framework incorporates three innovative components: First, the Backbone integrates a lightweight Convolutional Gated Transformer (CGF) module, which employs normalized gating mechanisms with residual connections, and a Dilated Feature Fusion (DFF) structure that enables progressive multi-scale context modeling through dilated convolutions. These components synergistically enhance small object perception and environmental context understanding without compromising network efficiency. Second, the neck features a hierarchical feature fusion module (HFFM) that establishes guided feature aggregation paths through hierarchical structuring, facilitating collaborative modeling between local structural information and global semantics for robust multi-scale object detection in complex traffic scenarios. Third, the head implements a shared feature detection head (SFDH) structure, incorporating shared convolution modules for efficient cross-scale feature sharing and detail enhancement branches for improved texture and edge modeling. Extensive experiments validate the effectiveness of LEAD-YOLO: on the nuImages dataset, the method achieves 3.8% and 5.4% improvements in mAP@0.5 and mAP@[0.5:0.95], respectively, while reducing parameters by 24.1%. On the VisDrone2019 dataset, performance gains reach 7.9% and 6.4% for corresponding metrics. These findings demonstrate that LEAD-YOLO achieves an excellent balance between detection accuracy and model efficiency, thereby showcasing substantial potential for applications in autonomous driving. |
| format | Article |
| id | doaj-art-399e9ec8b3464e148cd98d2288c2d744 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-399e9ec8b3464e148cd98d2288c2d7442025-08-20T03:36:23ZengMDPI AGSensors1424-82202025-08-012515480010.3390/s25154800LEAD-YOLO: A Lightweight and Accurate Network for Small Object Detection in Autonomous DrivingYunchuan Yang0Shubin Yang1Qiqing Chan2School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, ChinaSchool of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, ChinaSchool of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, ChinaThe accurate detection of small objects remains a critical challenge in autonomous driving systems, where improving detection performance typically comes at the cost of increased model complexity, conflicting with the lightweight requirements of edge deployment. To address this dilemma, this paper proposes LEAD-YOLO (Lightweight Efficient Autonomous Driving YOLO), an enhanced network architecture based on YOLOv11n that achieves superior small object detection while maintaining computational efficiency. The proposed framework incorporates three innovative components: First, the Backbone integrates a lightweight Convolutional Gated Transformer (CGF) module, which employs normalized gating mechanisms with residual connections, and a Dilated Feature Fusion (DFF) structure that enables progressive multi-scale context modeling through dilated convolutions. These components synergistically enhance small object perception and environmental context understanding without compromising network efficiency. Second, the neck features a hierarchical feature fusion module (HFFM) that establishes guided feature aggregation paths through hierarchical structuring, facilitating collaborative modeling between local structural information and global semantics for robust multi-scale object detection in complex traffic scenarios. Third, the head implements a shared feature detection head (SFDH) structure, incorporating shared convolution modules for efficient cross-scale feature sharing and detail enhancement branches for improved texture and edge modeling. Extensive experiments validate the effectiveness of LEAD-YOLO: on the nuImages dataset, the method achieves 3.8% and 5.4% improvements in mAP@0.5 and mAP@[0.5:0.95], respectively, while reducing parameters by 24.1%. On the VisDrone2019 dataset, performance gains reach 7.9% and 6.4% for corresponding metrics. These findings demonstrate that LEAD-YOLO achieves an excellent balance between detection accuracy and model efficiency, thereby showcasing substantial potential for applications in autonomous driving.https://www.mdpi.com/1424-8220/25/15/4800autonomous drivingobject detectionYOLOv11nsmall objectlightweight |
| spellingShingle | Yunchuan Yang Shubin Yang Qiqing Chan LEAD-YOLO: A Lightweight and Accurate Network for Small Object Detection in Autonomous Driving Sensors autonomous driving object detection YOLOv11n small object lightweight |
| title | LEAD-YOLO: A Lightweight and Accurate Network for Small Object Detection in Autonomous Driving |
| title_full | LEAD-YOLO: A Lightweight and Accurate Network for Small Object Detection in Autonomous Driving |
| title_fullStr | LEAD-YOLO: A Lightweight and Accurate Network for Small Object Detection in Autonomous Driving |
| title_full_unstemmed | LEAD-YOLO: A Lightweight and Accurate Network for Small Object Detection in Autonomous Driving |
| title_short | LEAD-YOLO: A Lightweight and Accurate Network for Small Object Detection in Autonomous Driving |
| title_sort | lead yolo a lightweight and accurate network for small object detection in autonomous driving |
| topic | autonomous driving object detection YOLOv11n small object lightweight |
| url | https://www.mdpi.com/1424-8220/25/15/4800 |
| work_keys_str_mv | AT yunchuanyang leadyoloalightweightandaccuratenetworkforsmallobjectdetectioninautonomousdriving AT shubinyang leadyoloalightweightandaccuratenetworkforsmallobjectdetectioninautonomousdriving AT qiqingchan leadyoloalightweightandaccuratenetworkforsmallobjectdetectioninautonomousdriving |