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A Structurally Optimized and Efficient Lightweight Object Detection Model for Autonomous Driving
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DOI：10.3390/s26010054.png)
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Abstract
En 中文
Object detection plays a pivotal role in safety-critical applications, including autonomous driving, intelligent surveillance, and unmanned aerial systems. However, many state-of-the-art detectors remain highly resource-intensive; their large parameter sizes and substantial floating-point operations make it difficult to balance accuracy and efficiency, particularly under constrained computational budgets. To mitigate this accuracy–efficiency trade-off, we propose FE-YOLOv8, a lightweight yet more effective variant of YOLOv8 (You Only Look Once version 8). Specifically, two architectural refinements are introduced: (1) C2f-Faster (Cross-Stage-Partial 2-Conv Faster Block) modules embedded in both the backbone and neck, where PConv (partial convolution) prunes redundant computations without diminishing representational capacity; and (2) an EfficientHead detection head that integrates EMSConv (Efficient Multi-Scale Convolution) to enhance multi-scale feature fusion while simplifying the head design and maintaining low computational complexity. Extensive ablation and comparative experiments on the SODA-10M dataset show that FE-YOLOv8 reduces the parameter count by 31.09% and the computational cost by 43.31% relative to baseline YOLOv8 while achieving comparable or superior mean Average Precision (mAP). Generalization experiments conducted on the BDD100K dataset further validate these improvements, demonstrating that FE-YOLOv8 achieves a favorable balance between accuracy and efficiency within the YOLOv8 family and provides new architectural insights for lightweight object detector design.
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