CrackNex: a Few-shot Low-light Crack Segmentation Model Based on Retinex Theory for UAV Inspections

• URL: http://arxiv.org/abs/2403.03063v1
• Date: Tue, 5 Mar 2024 15:52:54 GMT
• Title: CrackNex: a Few-shot Low-light Crack Segmentation Model Based on Retinex Theory for UAV Inspections
• Authors: Zhen Yao, Jiawei Xu, Shuhang Hou, Mooi Choo Chuah
• Abstract summary: CrackNex is a framework that utilizes reflectance information based on Retinex Theory to help the model learn a unified illumination-invariant representation. We present the first benchmark dataset, LCSD, for low-light crack segmentation. LCSD consists of 102 well-illuminated crack images and 41 low-light crack images.
• Score: 9.27355428681897
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Routine visual inspections of concrete structures are imperative for upholding the safety and integrity of critical infrastructure. Such visual inspections sometimes happen under low-light conditions, e.g., checking for bridge health. Crack segmentation under such conditions is challenging due to the poor contrast between cracks and their surroundings. However, most deep learning methods are designed for well-illuminated crack images and hence their performance drops dramatically in low-light scenes. In addition, conventional approaches require many annotated low-light crack images which is time-consuming. In this paper, we address these challenges by proposing CrackNex, a framework that utilizes reflectance information based on Retinex Theory to help the model learn a unified illumination-invariant representation. Furthermore, we utilize few-shot segmentation to solve the inefficient training data problem. In CrackNex, both a support prototype and a reflectance prototype are extracted from the support set. Then, a prototype fusion module is designed to integrate the features from both prototypes. CrackNex outperforms the SOTA methods on multiple datasets. Additionally, we present the first benchmark dataset, LCSD, for low-light crack segmentation. LCSD consists of 102 well-illuminated crack images and 41 low-light crack images. The dataset and code are available at https://github.com/zy1296/CrackNex.

Related papers

• Segmentation tool for images of cracks [0.16492989697868887]
  This paper proposes a semi-automatic crack segmentation tool that eases the manual segmentation of cracks on images. Also, it can be used to measure the geometry of the crack. The proposed method outperforms fully automatic methods and shows potential to be an adequate alternative to the manual data annotation.
  arXiv  Detail & Related papers  (2024-03-28T15:23:52Z)
• CrackCLF: Automatic Pavement Crack Detection based on Closed-Loop Feedback [14.986335013488643]
  CrackCLF is a neural network model that learns to correct errors on its own. The proposed CLF can be defined as a plug and play module, which can be embedded into different neural network models to improve their performances.
  arXiv  Detail & Related papers  (2023-11-20T14:52:48Z)
• Unsupervised crack detection on complex stone masonry surfaces [0.0]
  In this article a crack detection methodology for stone masonry walls is presented. In the proposed approach, crack detection is approached as an unsupervised anomaly detection problem on RGB (Red Green Blue) image patches.
  arXiv  Detail & Related papers  (2023-03-31T12:07:23Z)
• Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement [96.09255345336639]
  We formulate a principled One-stage Retinex-based Framework (ORF) to enhance low-light images. ORF first estimates the illumination information to light up the low-light image and then restores the corruption to produce the enhanced image. Our algorithm, Retinexformer, significantly outperforms state-of-the-art methods on thirteen benchmarks.
  arXiv  Detail & Related papers  (2023-03-12T16:54:08Z)
• InfoNeRF: Ray Entropy Minimization for Few-Shot Neural Volume Rendering [55.70938412352287]
  We present an information-theoretic regularization technique for few-shot novel view synthesis based on neural implicit representation. The proposed approach minimizes potential reconstruction inconsistency that happens due to insufficient viewpoints. We achieve consistently improved performance compared to existing neural view synthesis methods by large margins on multiple standard benchmarks.
  arXiv  Detail & Related papers  (2021-12-31T11:56:01Z)
• Learning Deep Context-Sensitive Decomposition for Low-Light Image Enhancement [58.72667941107544]
  A typical framework is to simultaneously estimate the illumination and reflectance, but they disregard the scene-level contextual information encapsulated in feature spaces. We develop a new context-sensitive decomposition network architecture to exploit the scene-level contextual dependencies on spatial scales. We develop a lightweight CSDNet (named LiteCSDNet) by reducing the number of channels.
  arXiv  Detail & Related papers  (2021-12-09T06:25:30Z)
• A Weakly-Supervised Surface Crack Segmentation Method using Localisation with a Classifier and Thresholding [0.20999222360659603]
  Recent work has been addressing this problem by supporting structural maintenance measures using machine learning methods. Our work proposes a weakly supervised approach which leverages a CNN classifier to create surface crack segmentation maps. We focus on the ease of implementation of our method and it is shown to perform well on several surface crack datasets.
  arXiv  Detail & Related papers  (2021-09-01T15:58:13Z)
• Contrastive Prototype Learning with Augmented Embeddings for Few-Shot Learning [58.2091760793799]
  We propose a novel contrastive prototype learning with augmented embeddings (CPLAE) model. With a class prototype as an anchor, CPL aims to pull the query samples of the same class closer and those of different classes further away. Extensive experiments on several benchmarks demonstrate that our proposed CPLAE achieves new state-of-the-art.
  arXiv  Detail & Related papers  (2021-01-23T13:22:44Z)
• Advances in deep learning methods for pavement surface crack detection and identification with visible light visual images [0.0]
  Compared to NDT and health monitoring method for cracks in engineering structures, surface crack detection or identification based on visible light images is non-contact. This paper comprehensively summarizes the pavement crack public data sets, and the performance and effectiveness of surface crack detection and identification deep learning methods for embedded platform.
  arXiv  Detail & Related papers  (2020-12-29T11:10:12Z)
• Nighttime Dehazing with a Synthetic Benchmark [147.21955799938115]
  We propose a novel synthetic method called 3R to simulate nighttime hazy images from daytime clear images. We generate realistic nighttime hazy images by sampling real-world light colors from a prior empirical distribution. Experiment results demonstrate their superiority over state-of-the-art methods in terms of both image quality and runtime.
  arXiv  Detail & Related papers  (2020-08-10T02:16:46Z)
• The Power of Triply Complementary Priors for Image Compressive Sensing [89.14144796591685]
  We propose a joint low-rank deep (LRD) image model, which contains a pair of complementaryly trip priors. We then propose a novel hybrid plug-and-play framework based on the LRD model for image CS. To make the optimization tractable, a simple yet effective algorithm is proposed to solve the proposed H-based image CS problem.
  arXiv  Detail & Related papers  (2020-05-16T08:17:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.