mlsanityData to 7 Oct 2026

Fast and Accurate Road Crack Detection Based on Adaptive Cost-Sensitive Loss Function

arXiv 2106.15510 ↗First posted 29 Jun 2021cs.CVcited by 7

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Abstract (arXiv)

Numerous detection problems in computer vision, including road crack detection, suffer from exceedingly foreground-background imbalance. Fortunately, modification of loss function appears to solve this puzzle once and for all. In this paper, we propose a pixel-based adaptive weighted cross-entropy loss in conjunction with Jaccard distance to facilitate high-quality pixel-level road crack detection. Our work profoundly demonstrates the influence of loss functions on detection outcomes, and sheds light on the sophisticated consecutive improvements in the realm of crack detection. Specifically, to verify the effectiveness of the proposed loss, we conduct extensive experiments on four public databases, i.e., CrackForest, AigleRN, Crack360, and BJN260. Compared with the vanilla weighted cross-entropy, the proposed loss significantly speeds up the training process while retaining the test accuracy.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 17 of its 69 references that are in the map. First posted on arXiv 29 Jun 2021.

Builds on

17 of 69 references.

References · 17

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  5. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  6. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,210
  7. 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,483
  8. 2016TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systemscited by 660
  9. 2015Holistically-Nested Edge Detectioncited by 590
  10. 2015Cost Sensitive Learning of Deep Feature Representations from Imbalanced Datacited by 167
  11. 2016Richer Convolutional Features for Edge Detectioncited by 141
  12. 2019Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuningcited by 91
5 more
  1. 2018Automatically designing CNN architectures using genetic algorithm for image classificationcited by 48
  2. 2018Asymptotic Soft Filter Pruning for Deep Convolutional Neural Networkscited by 27
  3. 2018Automatic Pavement Crack Detection Based on Structured Prediction with the Convolutional Neural Networkcited by 21
  4. 2018Convolutional Neural Networks with Alternately Updated Cliquecited by 14
  5. 2017Detection of curved lines with B-COSFIRE filters: A case study on crack delineationcited by 3

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 1; 2025: 3.

7 papers.

  1. 2024Staircase Cascaded Fusion of Lightweight Local Pattern Recognition and Long-Range Dependencies for Structural Crack Segmentationcited by 6
  2. 2022What's Cracking? A Review and Analysis of Deep Learning Methods for Structural Crack Segmentation, Detection and Quantificationcited by 4
  3. 2021Rethinking Lightweight Convolutional Neural Networks for Efficient and High-quality Pavement Crack Detectioncited by 3
  4. 2025Segment Any Crack: Deep Semantic Segmentation Adaptation for Crack Detectioncited by 3
  5. 2025Deep Learning for Crack Detection: A Review of Learning Paradigms, Generalizability, and Datasetscited by 1
  6. 2023Global Context Aggregation Network for Lightweight Saliency Detection of Surface Defectscited by 0
  7. 2025ASRL:A robust loss function with potential for developmentcited by 0