LCA-Net: A Lightweight Network for Small Object Detection in Road Traffic Scenes
Detecting small and distant objects in road traffic scenarios remains challenging owing to limited pixel resolution, cluttered backgrounds, and resource constraints on edge computing platforms. This work presents LCA-Net, a computationally efficient framework for small object detection that balances accuracy with model complexity. The framework incorporates three complementary designs: an Adaptive Deformable Downsampling Module (ADDM) that merges asymmetric and deformable convolution operations to improve spatial feature encoding while explicitly accounting for the parameter and computational cost of offset and modulation-mask prediction; a Cross-Scale Feature Fusion Pyramid (CSFFP) specifically engineered for minute objects, which augments multi-scale feature learning and enhances detection of far-field small targets; and a Lightweight Feature-Gated Detection Head (LFGDH) that employs channel–spatial attention to selectively emphasize informative features, thereby reducing both parameter count and computational cost. On Udacity, LCA-Net improves mAP@0.5 by 2.3 percentage points; on VisDrone2019, it improves mAP@0.5 by 1.7 percentage points. Across both benchmarks, the complete model reduces the parameter count by 25.58% and GFLOPs by 16.05% relative to YOLOv8-N. On the RTX A6000, LCA-Net-N reduces forward-pass latency from 1.82 to 1.63 ms, increases throughput from 549 to 613 FPS, and lowers peak GPU memory from 1180 to 1015 MiB. These results demonstrate a favorable accuracy–efficiency trade-off for real-time traffic perception.