mlsanityData to 14 Oct 2026

SOD-YOLO: Enhancing YOLO-Based Detection of Small Objects in UAV Imagery

arXiv 2507.12727 ↗First posted 17 Jul 2025cs.CVcited by 1

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Abstract (arXiv)

Small object detection remains a challenging problem in the field of object detection. To address this challenge, we propose an enhanced YOLOv8-based model, SOD-YOLO. This model integrates an ASF mechanism in the neck to enhance multi-scale feature fusion, adds a Small Object Detection Layer (named P2) to provide higher-resolution feature maps for better small object detection, and employs Soft-NMS to refine confidence scores and retain true positives. Experimental results demonstrate that SOD-YOLO significantly improves detection performance, achieving a 36.1% increase in mAP50:95 and 20.6% increase in mAP50 on the VisDrone2019-DET dataset compared to the baseline model. These enhancements make SOD-YOLO a practical and efficient solution for small object detection in UAV imagery. Our source code, hyper-parameters, and model weights are available at https://github.com/iamwangxiaobai/SOD-YOLO.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 19 of its 23 references that are in the map. First posted on arXiv 17 Jul 2025.

Builds on

19 of 23 references.

References · 19

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  2. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  3. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  4. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  5. 2015SSD: Single Shot MultiBox Detectorcited by 3,130
  6. 2018YOLOv3: An Incremental Improvementcited by 2,659
  7. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,162
  8. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,630
  9. 2016YOLO9000: Better, Faster, Strongercited by 1,431
  10. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 982
  11. 2018Object Detection with Deep Learning: A Reviewcited by 528
  12. 2024YOLOv10: Real-Time End-to-End Object Detectioncited by 515
7 more
  1. 2024YOLOv9: Learning What You Want to Learn Using Programmable Gradient Informationcited by 425
  2. 2017Soft-NMS -- Improving Object Detection With One Line of Codecited by 382
  3. 2019CSPNet: A New Backbone that can Enhance Learning Capability of CNNcited by 381
  4. 2018The Unmanned Aerial Vehicle Benchmark: Object Detection and Trackingcited by 208
  5. 2017Perceptual Generative Adversarial Networks for Small Object Detectioncited by 76
  6. 2023ASF-YOLO: A Novel YOLO Model with Attentional Scale Sequence Fusion for Cell Instance Segmentationcited by 11
  7. 2023EdgeYOLO: An Edge-Real-Time Object Detectorcited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026DroneScan-YOLO: Redundancy-Aware Lightweight Detection for Tiny Objects in UAV Imagerycited by 0