Yang Yunchuan, Yang Shubin, Chan Qiqing
School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China.
Sensors (Basel). 2025 Aug 4;25(15):4800. doi: 10.3390/s25154800.
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.
在自动驾驶系统中，精确检测小物体仍然是一项关键挑战，在该系统中，提高检测性能通常以增加模型复杂度为代价，这与边缘部署的轻量级要求相冲突。为了解决这一困境，本文提出了LEAD - YOLO（轻量级高效自动驾驶YOLO），这是一种基于YOLOv11n的增强型网络架构，在保持计算效率的同时实现了卓越的小物体检测性能。所提出的框架包含三个创新组件：第一，主干网络集成了一个轻量级卷积门控变换器（CGF）模块，该模块采用带残差连接的归一化门控机制，以及一个扩张特征融合（DFF）结构，该结构通过扩张卷积实现渐进式多尺度上下文建模。这些组件协同增强了小物体感知和环境上下文理解，而不会损害网络效率。第二，颈部采用了分层特征融合模块（HFFM），该模块通过分层结构建立引导特征聚合路径，促进局部结构信息和全局语义之间的协同建模，以在复杂交通场景中进行强大的多尺度目标检测。第三，头部实现了共享特征检测头（SFDH）结构，包含用于高效跨尺度特征共享的共享卷积模块和用于改进纹理和边缘建模的细节增强分支。大量实验验证了LEAD - YOLO的有效性：在nuImages数据集上，该方法在mAP@0.5和mAP@[0.5:0.95]上分别实现了3.8%和5.4%的提升，同时参数减少了24.1%。在VisDrone2019数据集上，相应指标的性能提升分别达到7.9%和6.4%。这些发现表明，LEAD - YOLO在检测精度和模型效率之间实现了出色的平衡，从而展示了在自动驾驶应用中的巨大潜力。
