mlsanityData to 14 Oct 2026

High-Performance Fine Defect Detection in Artificial Leather Using Dual Feature Pool Object Detection

arXiv 2307.16751 ↗First posted 31 Jul 2023cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

In this study, the structural problems of the YOLOv5 model were analyzed emphatically. Based on the characteristics of fine defects in artificial leather, four innovative structures, namely DFP, IFF, AMP, and EOS, were designed. These advancements led to the proposal of a high-performance artificial leather fine defect detection model named YOLOD. YOLOD demonstrated outstanding performance on the artificial leather defect dataset, achieving an impressive increase of 11.7% - 13.5% in AP_50 compared to YOLOv5, along with a significant reduction of 5.2% - 7.2% in the error detection rate. Moreover, YOLOD also exhibited remarkable performance on the general MS-COCO dataset, with an increase of 0.4% - 2.6% in AP compared to YOLOv5, and a rise of 2.5% - 4.1% in AP_S compared to YOLOv5. These results demonstrate the superiority of YOLOD in both artificial leather defect detection and general object detection tasks, making it a highly efficient and effective model for real-world applications.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 33 of its 52 references that are in the map. First posted on arXiv 31 Jul 2023.

Builds on

33 of 52 references.

References · 33

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  3. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  4. 2017Mask R-CNNcited by 7,277
  5. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  6. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,183
  7. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 5,045
  8. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  9. 2015SSD: Single Shot MultiBox Detectorcited by 3,130
  10. 2015Fast R-CNNcited by 2,933
  11. 2018YOLOv3: An Incremental Improvementcited by 2,659
  12. 2018CBAM: Convolutional Block Attention Modulecited by 2,477
21 more
  1. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,162
  2. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,630
  3. 2016YOLO9000: Better, Faster, Strongercited by 1,431
  4. 2017Cascade R-CNN: Delving into High Quality Object Detectioncited by 1,276
  5. 2017Mixed Precision Trainingcited by 1,156
  6. 2021YOLOX: Exceeding YOLO Series in 2021cited by 1,071
  7. 2019EfficientDet: Scalable and Efficient Object Detectioncited by 1,020
  8. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 982
  9. 2018Path Aggregation Network for Instance Segmentationcited by 798
  10. 2014Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognitioncited by 720
  11. 2020Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentationcited by 596
  12. 2019Distance-IoU Loss: Faster and Better Learning for Bounding Box Regressioncited by 459
  13. 2016R-FCN: Object Detection via Region-based Fully Convolutional Networkscited by 414
  14. 2019CSPNet: A New Backbone that can Enhance Learning Capability of CNNcited by 381
  15. 2019Libra R-CNN: Towards Balanced Learning for Object Detectioncited by 255
  16. 2020Scaled-YOLOv4: Scaling Cross Stage Partial Networkcited by 211
  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. 2021PP-YOLOv2: A Practical Object Detectorcited by 30
  21. 2021A State-of-the-art Survey of Object Detection Techniques in Microorganism Image Analysis: From Classical Methods to Deep Learning Approachescited by 12

Built on by

Not cited yet.