mlsanityData to 14 Oct 2026

Edge-Constrained UAV Small-Object Detection with P2 Enhancement and Quantum-Inspired Lightweight Structure Search

arXiv 2606.09081 ↗First posted 8 Jun 2026cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

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

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 41 of its 62 references that are in the map. First posted on arXiv 8 Jun 2026.

Builds on

41 of 62 references.

References · 41

  1. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  2. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  3. 2020End-to-End Object Detection with Transformerscited by 7,414
  4. 2016Feature Pyramid Networks for Object Detectioncited by 5,036
  5. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  6. 2017Squeeze-and-Excitation Networkscited by 4,789
  7. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  8. 2015SSD: Single Shot MultiBox Detectorcited by 3,130
  9. 2018CBAM: Convolutional Block Attention Modulecited by 2,477
  10. 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,329
  11. 2018DARTS: Differentiable Architecture Searchcited by 1,798
  12. 2019FCOS: Fully Convolutional One-Stage Object Detectioncited by 1,577
29 more
  1. 2017Deformable Convolutional Networkscited by 1,488
  2. 2019Deep High-Resolution Representation Learning for Visual Recognitioncited by 1,420
  3. 2019Deep High-Resolution Representation Learning for Human Pose Estimationcited by 1,339
  4. 2021YOLOX: Exceeding YOLO Series in 2021cited by 1,071
  5. 2019EfficientDet: Scalable and Efficient Object Detectioncited by 1,020
  6. 2018MnasNet: Platform-Aware Neural Architecture Search for Mobilecited by 943
  7. 2021ByteTrack: Multi-Object Tracking by Associating Every Detection Boxcited by 823
  8. 2018Path Aggregation Network for Instance Segmentationcited by 798
  9. 2019Once-for-All: Train One Network and Specialize it for Efficient Deploymentcited by 787
  10. 2018ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardwarecited by 673
  11. 2017DOTA: A Large-scale Dataset for Object Detection in Aerial Imagescited by 623
  12. 2019Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selectioncited by 540
  13. 2018FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Searchcited by 485
  14. 2018Deep Learning for Generic Object Detection: A Surveycited by 392
  15. 2018AMC: AutoML for Model Compression and Acceleration on Mobile Devicescited by 378
  16. 2019NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detectioncited by 291
  17. 2019Libra R-CNN: Towards Balanced Learning for Object Detectioncited by 255
  18. 2021Coordinate Attention for Efficient Mobile Network Designcited by 241
  19. 2019Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computingcited by 228
  20. 2020DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolutioncited by 222
  21. 2018The Unmanned Aerial Vehicle Benchmark: Object Detection and Trackingcited by 208
  22. 2021Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Bettercited by 154
  23. 2019Convergence of Edge Computing and Deep Learning: A Comprehensive Surveycited by 120
  24. 2019Clustered Object Detection in Aerial Imagescited by 83
  25. 2018Vision Meets Drones: A Challengecited by 81
  26. 2016A Survey on Object Detection in Optical Remote Sensing Imagescited by 78
  27. 2020AU-AIR: A Multi-modal Unmanned Aerial Vehicle Dataset for Low Altitude Traffic Surveillancecited by 56
  28. 2022Feature Selection on Quantum Computerscited by 21
  29. 2024Quantum-Inspired Evolutionary Algorithms for Feature Subset Selection: A Comprehensive Surveycited by 2

Built on by

Not cited yet.