What question did this study set out to answer?

This study aims to improve visual object detection accuracy while maintaining model efficiency for autonomous driving.

July 22, 2026Open Access

YOLOv8n-FEL: A Lightweight Visual Detector with a Progressive Compress-Then-Enhance Architecture for Autonomous Driving

LLLei LiBeijing Institute of Technology XCXingrong ChengXihua University XYXiaofeng YinXihua University

Key Points

This study aims to improve visual object detection accuracy while maintaining model efficiency for autonomous driving.
Developed YOLOv8n-FEL with a compress-then-enhance architecture.
Introduced FasterBlock and Integrated Efficient Multi-Scale Attention (EMA) to reduce complexity.
Conducted experiments on the KITTI dataset to assess performance improvements.
Parameter count reduced from 3.006 M to 2.582 M (14.1% reduction).
Precision improved from 88.8% to 90.6% and mAP@0.5 from 89.1% to 89.5%.
AP@0.5 for Cyclist detection increased from 89.9% to 92.6%, with challenges remaining for Pedestrian detection.

Abstract

Real-time visual object detection on resource-constrained in-vehicle platforms requires a practical balance among detection accuracy, model size, computational complexity, and inference speed. Existing lightweight detectors may sacrifice accuracy, particularly for small and occluded objects, when model scale is compressed aggressively. To address this trade-off, this study investigates YOLOv8n-FEL, a lightweight visual detector built on YOLOv8n with a progressive “compress-then-enhance” architecture. The design contains three architecture-level modifications. First, the standard Bottleneck blocks in the C2f module are replaced with FasterBlock from FasterNet, forming the C2F-Faster module to reduce parameter count and computational cost. Second, Efficient Multi-Scale Attention (EMA) is integrated through the C2F-Faster-EMA configuration to refine features for small and occluded objects. Third, Large Separable Kernel Attention (LSKA) is introduced at the SPPF stage to refine multi-scale pooled features without quadratic complexity growth. Experiments on KITTI show that YOLOv8n-FEL reduces the parameter count from 3.006 M to 2.582 M and GFLOPs from 8.1 to 6.7, corresponding to reductions of approximately 14.1% and 17.3%, respectively. Under this lightweight setting, precision increases from 88.8% to 90.6% and mAP@0.5 increases from 89.1% to 89.5%; however, recall, mAP@0.5:0.95, and measured inference latency do not surpass the YOLOv8n baseline. At the class level, the main gain is observed for Cyclist detection, with AP@0.5 increasing from 89.9% to 92.6%, whereas Pedestrian detection and high-IoU localization remain challenging. Direct evaluation on DAIR-V2X and Carla simulation is used as supporting evidence under domain shift and controlled scenario variations rather than as deployment-level robustness validation. Overall, YOLOv8n-FEL is best understood as a lightweight accuracy–complexity trade-off model.

Perguntar à IA

Bookmark

View Full Paper