mlsanityData to 7 Oct 2026

YOLOCS: Object Detection based on Dense Channel Compression for Feature Spatial Solidification

arXiv 2305.04170 ↗First posted 7 May 2023cs.CVcited by 4

Not on the map. Why?

Abstract (arXiv)

In this study, we examine the associations between channel features and convolutional kernels during the processes of feature purification and gradient backpropagation, with a focus on the forward and backward propagation within the network. Consequently, we propose a method called Dense Channel Compression for Feature Spatial Solidification. Drawing upon the central concept of this method, we introduce two innovative modules for backbone and head networks: the Dense Channel Compression for Feature Spatial Solidification Structure (DCFS) and the Asymmetric Multi-Level Compression Decoupled Head (ADH). When integrated into the YOLOv5 model, these two modules demonstrate exceptional performance, resulting in a modified model referred to as YOLOCS. Evaluated on the MSCOCO dataset, the large, medium, and small YOLOCS models yield AP of 50.1%, 47.6%, and 42.5%, respectively. Maintaining inference speeds remarkably similar to those of the YOLOv5 model, the large, medium, and small YOLOCS models surpass the YOLOv5 model's AP by 1.1%, 2.3%, and 5.2%, respectively.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 34 of its 56 references that are in the map. First posted on arXiv 7 May 2023.

Builds on

34 of 56 references.

References · 34

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  3. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  4. 2017Mask R-CNNcited by 7,259
  5. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  6. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  7. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  8. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  9. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  10. 2015Fast R-CNNcited by 2,930
  11. 2018YOLOv3: An Incremental Improvementcited by 2,652
  12. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,160
22 more
  1. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,624
  2. 2016YOLO9000: Better, Faster, Strongercited by 1,429
  3. 2017Cascade R-CNN: Delving into High Quality Object Detectioncited by 1,275
  4. 2017Mixed Precision Trainingcited by 1,135
  5. 2021YOLOX: Exceeding YOLO Series in 2021cited by 1,064
  6. 2019EfficientDet: Scalable and Efficient Object Detectioncited by 1,018
  7. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 978
  8. 2018Path Aggregation Network for Instance Segmentationcited by 796
  9. 2014Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognitioncited by 719
  10. 2020Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentationcited by 595
  11. 2019Distance-IoU Loss: Faster and Better Learning for Bounding Box Regressioncited by 454
  12. 2016R-FCN: Object Detection via Region-based Fully Convolutional Networkscited by 414
  13. 2019CSPNet: A New Backbone that can Enhance Learning Capability of CNNcited by 381
  14. 2019Libra R-CNN: Towards Balanced Learning for Object Detectioncited by 255
  15. 2020Scaled-YOLOv4: Scaling Cross Stage Partial Networkcited by 211
  16. 2019An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detectioncited by 163
  17. 2021You Only Look One-level Featurecited by 116
  18. 2019Learning Spatial Fusion for Single-Shot Object Detectioncited by 59
  19. 2020PP-YOLO: An Effective and Efficient Implementation of Object Detectorcited by 44
  20. 2022Designing Network Design Strategies Through Gradient Path Analysiscited by 32
  21. 2021PP-YOLOv2: A Practical Object Detectorcited by 30
  22. 2022Boosting Few-shot Fine-grained Recognition with Background Suppression and Foreground Alignmentcited by 29

Built on by

Cited by year

Citing papers per year: 2024: 3; 2025: 1.

4 papers.

  1. 2024YOLOv9: Learning What You Want to Learn Using Programmable Gradient Informationcited by 422
  2. 2024Hyper-YOLO: When Visual Object Detection Meets Hypergraph Computationcited by 29
  3. 2024FA-YOLO: Research On Efficient Feature Selection YOLO Improved Algorithm Based On FMDS and AGMF Modulescited by 1
  4. 2025YOLO-PRO: Enhancing Instance-Specific Object Detection with Full-Channel Global Self-Attentioncited by 0