Edge-Constrained UAV Small-Object Detection with P2 Enhancement and Quantum-Inspired Lightweight Structure Search
Researchers have developed a new method for improving object detection in unmanned aerial vehicles (UAVs) under strict computational constraints. The approach combines a P2 high-resolution detection branch with a quantum-inspired evolutionary algorithm (QIEA) to efficiently screen for lightweight network structures. This enhancement significantly boosts the detection of small objects, outperforming existing baselines on the VisDrone dataset. AI
IMPACT Improves efficiency and accuracy for real-time object detection in resource-constrained environments like UAVs.