针对无人机小目标检测，提出P2增强与量子启发轻量化结构搜索方法。
Edge-Constrained UAV Small-Object Detection with P2 Enhancement and Quantum-Inspired Lightweight Structure Search
- 引入P2高分辨率分支保留浅层细节，提升小目标感知能力
- 用量子启发算法筛选模型，使小目标召回率提升31.10%，精度增44.9%
- 适合边缘部署场景，尤其对资源受限的无人机视觉系统有实用价值
无人机小目标检测需在机载算力和内存约束下使用紧凑型检测器。轻量网络中反复下采样会削弱浅层空间信息，手动添加注意力或融合模块可能增加开销且收益不稳定。本研究在边缘部署约束下分析YOLOX-Nano，结合P2高分辨率检测分支与量子启发进化算法（QIEA）进行轻量化结构筛选。搜索空间以轻量优先与任务特异性为设计原则，评估综合考虑准确率、浮点运算量（FLOPs）、延迟、内存消耗和召回率。在VisDrone数据集上，P2分支使小目标平均精度（APamall）相比基线提升31.10%；相较于相似规模的NanoDet-Plus，YOLOX-Nano+-P2在小目标精度（APs0.ss）上提升17.5%，大目标精度（APamal）提升44.9%。QIEA选出的候选模型召回率最高，但经完整训练后，+P2仍为最优精度方案。对随机最佳、遗传算法最佳及SA/QUBO最佳候选的100轮验证表明，代理排名未必转化为最终性能。结果支持将P2作为小目标增强主路径，QIEA作为轻量化候选筛选与精度-成本分析工具。代码、配置文件、诊断脚本与汇总结果已公开于https://github.com/Ming23233/UAV-QIEA-Edge-Detection。
原文摘要 · Abstract (English)
Unmanned aerial vehicle (UAV) object detection requires compact detectors that retain small-object details under onboard computation and memory constraints. Repeated downsampling inlightweight networks weakens shallow spatial information, while manually adding attention orfusion modules may increase cost without stable gains. This study analyzes YOLOX-Nano underedge-deployment constraints by combining a P2 high-resolution detection branch with a quantum-inspired evolutionary algorithm (QIEA) for lightweight structure screening. The search space isdefined by lightweight priority and task specificity, and the evaluation jointly considers accuracy,floating-point operations (FLOPs), latency, memory consumption, and recall. On VisDrone, theP2 branch increases APamall by 31.10% over the YOLOX-Nano baseline. Compared with NanoDet-Plus with similar model size, YOLOX-Nano+-P2 improves APs0.ss by 17.5% and APamal by 44.9%.The QIEA-selected candidate obtains the highest Recallso, but +P2 remains the strongest AP-oriented variant after full training. Full 100-epoch verification of Random-best, GA-best, andSA/QUBO-best candidates further shows that proxy rankings do not necessarily transfer to finalAPse9s. These results support using P2 as the main small-object enhancement path and QIEA as alightweight tool for candidate screening and accuracy-cost analysis. The source code, configurationfiles, diagnostic scripts, and summarized results are available at https://github.com/Ming23233/UAV-QIEA-Edge-Detection
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。