mlsanityData to 7 Oct 2026

EdgeYOLO: An Edge-Real-Time Object Detector

arXiv 2302.07483 ↗First posted 15 Feb 2023cs.CVcited by 6

Not on the map. Why?

Abstract (arXiv)

This paper proposes an efficient, low-complexity and anchor-free object detector based on the state-of-the-art YOLO framework, which can be implemented in real time on edge computing platforms. We develop an enhanced data augmentation method to effectively suppress overfitting during training, and design a hybrid random loss function to improve the detection accuracy of small objects. Inspired by FCOS, a lighter and more efficient decoupled head is proposed, and its inference speed can be improved with little loss of precision. Our baseline model can reach the accuracy of 50.6% AP50:95 and 69.8% AP50 in MS COCO2017 dataset, 26.4% AP50:95 and 44.8% AP50 in VisDrone2019-DET dataset, and it meets real-time requirements (FPS>=30) on edge-computing device Nvidia Jetson AGX Xavier. We also designed lighter models with less parameters for edge computing devices with lower computing power, which also show better performances. Our source code, hyper-parameters and model weights are all available at https://github.com/LSH9832/edgeyolo.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 23 of its 32 references that are in the map. First posted on arXiv 15 Feb 2023.

Builds on

23 of 32 references.

References · 23

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  3. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  4. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  5. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
  6. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  7. 2018YOLOv3: An Incremental Improvementcited by 2,652
  8. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,160
  9. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,624
  10. 2019FCOS: Fully Convolutional One-Stage Object Detectioncited by 1,573
  11. 2016YOLO9000: Better, Faster, Strongercited by 1,429
  12. 2017ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devicescited by 1,194
11 more
  1. 2021YOLOX: Exceeding YOLO Series in 2021cited by 1,064
  2. 2019EfficientDet: Scalable and Efficient Object Detectioncited by 1,018
  3. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 978
  4. 2021RepVGG: Making VGG-style ConvNets Great Againcited by 520
  5. 2022YOLOv6: A Single-Stage Object Detection Framework for Industrial Applicationscited by 273
  6. 2021You Only Learn One Representation: Unified Network for Multiple Taskscited by 86
  7. 2018Vision Meets Drones: A Challengecited by 80
  8. 2019Augmentation for small object detectioncited by 73
  9. 2023YOLOv6 v3.0: A Full-Scale Reloadingcited by 58
  10. 2021Densely Nested Top-Down Flows for Salient Object Detectioncited by 23
  11. 2020CopyPaste: An Augmentation Method for Speech Emotion Recognitioncited by 10

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 1.

6 papers.

  1. 2025SOD-YOLO: Enhancing YOLO-Based Detection of Small Objects in UAV Imagerycited by 1
  2. 2025EDNet: Edge-Optimized Small Target Detection in UAV Imagery -- Faster Context Attention, Better Feature Fusion, and Hardware Accelerationcited by 0
  3. 2025Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Designcited by 0
  4. 2025SBP-YOLO:A Lightweight Real-Time Model for Detecting Speed Bumps and Potholes toward Intelligent Vehicle Suspension Systemscited by 0
  5. 2025SPGrasp: Spatiotemporal Prompt-driven Grasp Synthesis in Dynamic Scenescited by 0
  6. 2026How Much Does It Cost to Answer My Question? Benchmarking Cloud VLM-based VQA Systemscited by 0