SDRNet: An end-to-end shadow detection and removal network

Tang, Jin; Luo, Qinghua; Guo, Fan; Wu, Zhihu; Xiao, Xiaoming; Gao, Yan

doi:10.1016/j.image.2020.115832

Cited by 8 publications

(3 citation statements)

References 24 publications

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“…Balanced error rate is used as the performance measure. Tang et al [22] suggested an end-to-end SDRNet shadow detection and removal network. The proposed network is based on an encoder-decoder structure.…”

Section: Related Workmentioning

confidence: 99%

Shadow detection from images using fuzzy logic and PCPerturNet

Chaki

2021

IET Image Processing

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Shadow detection is a challenging and essential task for interpreting the scene. Regardless of encouraging findings from current Deep Learning (DL) approaches used for shadow detection, the methods are also dealing with inconsistent situations where the visual representation of non-shadow and shadow regions is equivalent. In this article, a DL based approach is introduced for image pixel-level shadow detection. The proposed CNN-based approach, pattern conserver convolutional neural network (PCPerturNet) profits from a new design where shadow features are defined utilizing an effective skip-connection mapping arrangement. To make PCPerturNet robust from the change in brightness and contrast, several perturbed instances are generated by using a fuzzy-logic based method to train the system. Also, five types of augmentations are applied to images during training to make the system robust from the change in scale, orientation and flip. PCPerturNet derives and conserves shadow patterns in manifold layers and uses those layers progressively in several units to produce the shadow mask. The output of the proposed method is tested on two freely accessible databases and one self-created database where the accuracy rate obtained is 96.4%, 96.8%, and 89.4% which indicates that the proposed method outperforms the other shadow detection approaches used in the literature.

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Section: Related Workmentioning

confidence: 99%

Shadow detection from images using fuzzy logic and PCPerturNet

Chaki

2021

IET Image Processing

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“…In recent years, with the rapid development of deep learning methods, important progress has been made in the removal of rain, fog, and shadows in images [13]- [15]. In the field of civil engineering, deep learning methods are now widely used in rebar identifications, signal denoising, concrete defect identifications, and so on.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Rebar Clutters Removal and Defect Echoes Enhancement in GPR Images

et al. 2021

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The clutters of rebar in the ground penetrating radar (GPR) images may mask the echoes of the inner defects under the rebars, which adversely affects the identification of the inner structural defects in the reinforced concrete (RC). In this study, a deep learning-based method for rebar clutters removal and defect echoes enhancement in GPR B-scan images was proposed. The residual-inception blocks and attention modules were designed in the network as per the characteristics of the task. Validation of the method has been performed at three levels: first using synthetic data, next by synthetic data superimposed with actual concrete background noise, and finally using a sandbox model test in a realistic scenario. The results indicate that the proposed method had the ability to effectively remove rebar clutters and reconstruct complete defect echoes, with performance superior to those of the traditional deep-learning based methods. A RC structural defect identification experiment was performed to verify the effectiveness of the proposed method. The results indicated that after the rebar signals were removed using the method proposed, the accuracy for identifying the defects under the rebars had been improved from 0.208 to 0.850. The proposed method is promising for applying to practical engineering due to its fast processing and better performance.INDEX TERMS Deep learning, defect identification, ground penetrating radar (GPR), rebar.

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“…Furthermore, a deep learning-related approach that uses generative adversarial networks (GANs) is used by Inuoue et al [35] to perform the shadow detection and removal process, where they proposed a SynShadow model, a large-scale dataset of shadow/shadow-free/matte image triplets, and the pipeline to synthesize the diverse and realistic triplets. An adversarial neural network (ANN) based solution has been recently proposed by Tang et al [36]. The authors targeted obtaining a shadow detection and removal procedure by taking care of the image color consistency at the mask silhouette region.…”

Section: Introductionmentioning

confidence: 99%

A Novel Shadow Removal Method Based upon Color Transfer and Color Tuning in UAV Imaging

et al. 2021

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Through the increasing use of unmanned aerial vehicles as remote sensing tools, shadows become evident in aerial imaging; this fact, alongside the higher spatial resolution obtained by high-resolution mounted cameras, presents a challenging issue when performing different image processing tasks related to urban areas monitoring. Accordingly, the state-of-the-art reported works can correct the shadow regions, but the heterogeneity between the corrected shadow and non-shadow areas is still evident and especially noticeable in concrete and asphalt regions. The present work introduces a local color transfer methodology to shadow removal which is based on the CIE L*a*b (Lightness, a and b) color space that considers chromatic differences in urban regions, and it is followed by a color tuning using the HSV color space. The quantitative comparison was executed by using the shadow standard deviation index (SSDI), where the proposed work provided low values that improve up to 19 units regarding other tested methods. The qualitative comparison was visually realized and proved that the proposed method enhances the color correspondence without losing texture information. Quantitative and qualitative results validate the results of color correction and texture preservation accuracy of the proposed method against other published methodologies.

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SDRNet: An end-to-end shadow detection and removal network

Cited by 8 publications

References 24 publications

Shadow detection from images using fuzzy logic and PCPerturNet

Shadow detection from images using fuzzy logic and PCPerturNet

Deep Learning-Based Rebar Clutters Removal and Defect Echoes Enhancement in GPR Images

A Novel Shadow Removal Method Based upon Color Transfer and Color Tuning in UAV Imaging

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