A novel fully convolutional network based on marker-controlled watershed segmentation algorithm for industrial soot robot target segmentation

Ju, Aiyun; Wang, Zhongli

doi:10.1007/s12065-022-00708-z

Cited by 6 publications

(6 citation statements)

References 32 publications

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“…The experiment sults indicated that the proposed method achieved substantial improvements comp with other state-of-the-art methods [38]. Other studies have combined MCW with a learning model to enhance the segmentation efficiency and improve accuracy [39,40 The extraction of coastal aquaculture ponds is similar to the segmentation of n cells in medical images. Both aquaculture ponds and the nuclei cells are concentrate densely distributed, with distinct spatial geometrical features, where the ponds are lar rectangles and the cells are round or elliptical.…”

Section: Study Areamentioning

confidence: 88%

“…The experimental results indicated that the proposed method achieved substantial improvements compared with other state-of-the-art methods [38]. Other studies have combined MCW with a deep learning model to enhance the segmentation efficiency and improve accuracy [39][40][41].…”

Section: Introductionmentioning

confidence: 85%

“…The aquaculture extraction accuracy is quantitatively evaluated with four accuracy metrics: F1 score, mean intersection over union (IoU), matching rate (MR) and instantiated F1 score (insF1). F1 and IoU are pixel-based metrics, presenting the overall classification accuracy of the results, and are calculated based on a statistical analysis of the classified pixels to evaluate the accuracy of semantic segmentation [40]. The calculations are as follows: In this study, we combine UNet++ with MCW segmentation and introduce the boundary patch refinement (BPR) postprocessing method to propose a novel framework for coastal aquaculture extraction, called MCW (3 BPR , 2 BPR ), where 3 represents the three categories in the marker prediction (foreground, background and dikes), 2 represents the two categories in the foreground prediction (aquaculture and nonaquaculture) 3 BPR and 2 BPR indicate that BPR postprocessing is applied in the UNet++ m and UNet++ f subnetworks, respectively.…”

Section: Accuracy Assessment and Comparisonmentioning

confidence: 99%

Section: Accuracy Assessment and Comparisonmentioning

confidence: 99%

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Coastal Aquaculture Extraction Using GF-3 Fully Polarimetric SAR Imagery: A Framework Integrating UNet++ with Marker-Controlled Watershed Segmentation

Yang

et al. 2023

Remote Sensing

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Coastal aquaculture monitoring is vital for sustainable offshore aquaculture management. However, the dense distribution and various sizes of aquacultures make it challenging to accurately extract the boundaries of aquaculture ponds. In this study, we develop a novel combined framework that integrates UNet++ with a marker-controlled watershed segmentation strategy to facilitate aquaculture boundary extraction from fully polarimetric GaoFen-3 SAR imagery. First, four polarimetric decomposition algorithms were applied to extract 13 polarimetric scattering features. Together with the nine other polarisation and texture features, a total of 22 polarimetric features were then extracted, among which four were optimised according to the separability index. Subsequently, to reduce the “adhesion” phenomenon and separate adjacent and even adhering ponds into individual aquaculture units, two UNet++ subnetworks were utilised to construct the marker and foreground functions, the results of which were then used in the marker-controlled watershed algorithm to obtain refined aquaculture results. A multiclass segmentation strategy that divides the intermediate markers into three categories (aquaculture, background and dikes) was applied to the marker function. In addition, a boundary patch refinement postprocessing strategy was applied to the two subnetworks to extract and repair the complex/error-prone boundaries of the aquaculture ponds, followed by a morphological operation that was conducted for label augmentation. An experimental investigation performed to extract individual aquacultures in the Yancheng Coastal Wetlands indicated that the crucial features for aquacultures are Shannon entropy (SE), the intensity component of SE (SE_I) and the corresponding mean texture features (Mean_SE and Mean_SE_I). When the optimal features were introduced, our proposed method performed better than standard UNet++ in aquaculture extraction, achieving improvements of 1.8%, 3.2%, 21.7% and 12.1% in F1, IoU, MR and insF1, respectively. The experimental results indicate that the proposed method can handle the adhesion of both adjacent objects and unclear boundaries effectively and capture clear and refined aquaculture boundaries.

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Section: Study Areamentioning

confidence: 88%

Section: Introductionmentioning

confidence: 85%

Section: Accuracy Assessment and Comparisonmentioning

confidence: 99%

Section: Accuracy Assessment and Comparisonmentioning

confidence: 99%

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Coastal Aquaculture Extraction Using GF-3 Fully Polarimetric SAR Imagery: A Framework Integrating UNet++ with Marker-Controlled Watershed Segmentation

Yang

et al. 2023

Remote Sensing

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“…Marker-based watershed algorithm is widely used to perform more reasonable segmentation of the target. The commonly used marker-based watershed algorithms include morphological watershed (Ju et al, 2022), distance transform watershed (Guo et al, 2022), and extreme erosion watershed (Abrol et al, 2021). The difference between these three algorithms is that they adopt different target foreground marking methods.…”

Section: Introductionmentioning

confidence: 99%

DE-XRT coal preparation image overlapping and adhesion particle segmentation method

Li¹,

Wang²,

Guo³

2022

Physicochem. Probl. Miner. Process.

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Accurate segmentation of dual-energy X-ray transmission (DE-XRT) coal and gangue image regions are a prerequisite for feature extraction, identification, localization, and separation. A watershed algorithm based on multi-grayscale threshold segmentation (MGTS) is proposed to mark the foreground for the adhesion and overlap of coal and gangue. The grayscale images of foreground objects are segmented using multiple grayscale thresholds, and the number of connected domains is recorded each time. As the gray threshold value decreases, overlapping and adhering objects are gradually separated. The binary image segmented at the grayscale threshold with the most significant number of connected domains is used as a marker region. This marker region is used as the seed point of the watershed algorithm to find the dividing line. The experimental results show that the segmentation accuracy is 91.35%, and the segmentation accuracy of overlapping adhesions of 2, 3, and 4 targets is higher than 90%.

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Comparative Image Analysis of Apple and Samsung Devices: A Technical Perspective

Kazmi,

Smith,

Amer

et al. 2023

2023 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)

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A novel fully convolutional network based on marker-controlled watershed segmentation algorithm for industrial soot robot target segmentation

Cited by 6 publications

References 32 publications

Coastal Aquaculture Extraction Using GF-3 Fully Polarimetric SAR Imagery: A Framework Integrating UNet++ with Marker-Controlled Watershed Segmentation

Coastal Aquaculture Extraction Using GF-3 Fully Polarimetric SAR Imagery: A Framework Integrating UNet++ with Marker-Controlled Watershed Segmentation

DE-XRT coal preparation image overlapping and adhesion particle segmentation method

Comparative Image Analysis of Apple and Samsung Devices: A Technical Perspective

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