Optimized Deep Encoder-Decoder Methods for Crack Segmentation

Abstract

Surface crack segmentation poses a challenging computer vision task as background, shape, colour and size of cracks vary. In this work we propose optimized deep encoder-decoder methods consisting of a combination of techniques which yield an increase in crack segmentation performance. Specifically we propose a decoder-part for an encoder-decoder based deep learning architecture for semantic segmentation and study its components to achieve increased performance. We also examine the use of different encoder strategies and introduce a data augmentation policy to increase the amount of available training data. The performance evaluation of our method is carried out on four publicly available crack segmentation datasets. Additionally, we introduce two techniques into the field of surface crack segmentation, previously not used there: Generating results using test-time-augmentation and performing a statistical result analysis over multiple training runs. The former approach generally yields increased performance results, whereas the latter allows for more reproducible and better representability of a methods results. Using those aforementioned strategies with our proposed encoder-decoder architecture we are able to achieve new state of the art results in all datasets.

Accepted Manuscript at Digital Signal Processing (Elsevier)

References in corpus (5)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
• Fully Convolutional Networks for Semantic Segmentation
• Ensemble of Deep Convolutional Neural Networks for Automatic Pavement Crack Detection and Measurement
• FPCNet: Fast Pavement Crack Detection Network Based on Encoder-Decoder Architecture

Cited by in corpus (4)

• Hybrid-Segmentor: A Hybrid Approach to Automated Fine-Grained Crack Segmentation in Civil Infrastructure
• Deep Learning for Segmentation of Cracks in High-Resolution Images of Steel Bridges
• CrackNex: a Few-shot Low-light Crack Segmentation Model Based on Retinex Theory for UAV Inspections
• SCNet: A Generalized Attention-based Model for Crack Fault Segmentation