Search

Tunnel disease detection and maintenance are critical tasks in urban engineering, and are essential for the safety and stability of urban transportation systems. Water stain detection presents unique challenges due to its variable morphology and scale, which leads to insufficient multiscale contextual information extraction and boundary information loss in complex environments. To address these challenges, this paper proposes a method called Deep Aggregation Network with Edge Information Supplement (DAEiS-Net) for detecting tunnel water stains. The proposed method employs a classic encoder–decoder architecture. Specifically, in the encoder part, a Deep Aggregation Module (DAM) is introduced to enhance feature representation capabilities. Additionally, a Multiscale Cross-Attention Module (MCAM) is proposed to suppress noise in the shallow features and enhance the texture information of the high-level features. Moreover, an Edge Information Supplement Module (EISM) is designed to mitigate semantic gaps across different stages of feature extraction, improving the extraction of water stain edge information. Furthermore, a Sub-Pixel Module (SPM) is proposed to fuse features at various scales, enhancing edge feature representation. Finally, we introduce the Tunnel Water Stain Dataset (TWS), specifically designed for tunnel water stain segmentation. Experimental results on the TWS dataset demonstrate that DAEiS-Net achieves state-of-the-art performance in tunnel water stain segmentation. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In remote sensing image segmentation tasks, various challenges arise, including difficulties in recognizing objects due to differences in perspective, difficulty in distinguishing objects with similar colors, and challenges in segmentation caused by occlusions. To address these issues, we propose a method called the grouped multiview aggregation network (GVANet), which leverages multiview information for image analysis. This approach enables global multiview expansion and fine-grained cross-layer information interaction within the network. Within this network framework, to better utilize a wider range of multiview information to tackle challenges in remote sensing segmentation, we introduce the multiview feature aggregation block for extracting multiview information. Furthermore, to overcome the limitations of same-level shortcuts when dealing with multiview problems, we propose the channel group fusion block for cross-layer feature information interaction through a grouped fusion approach. Finally, to enhance the utilization of global features during the feature reconstruction phase, we introduce the aggregation-inhibition-activation block for feature selection and focus, which captures the key features for segmentation. Comprehensive experimental results on the Vaihingen and Potsdam datasets demonstrate that GVANet outperforms current state-of-the-art methods, achieving mIoU scores of 84.5% and 87.6%, respectively.

Copyright of Journal of Guangxi Normal University - Natural Science Edition is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cracks in the concrete pavement will affect the safety,applicability,and durability of the structure,and crack detection is a challenging research hotspot.This paper proposes a crack detection model composed of an improved full convolutional network and a deep supervision network,which uses the improved VGG-16 as the backbone network.Firstly,the low-level convolutional feature aggregation is fused to the backbone network again through the spatial attention mechanism.Secondly,the middle and high-level convolutional features are fused through the lightweight dilated convolution fusion module for multi-feature fusion to get the clear edge and high-resolution feature maps,all side feature maps are added to produce the final prediction map.Finally,the deep supervision network provides direct supervision for the detection results of each stage.In this paper,the focus loss function is selected as the evaluation function,and the trained network model can efficiently identify the crack location from the input original image under various conditions such as uneven illumination and complex background.To verify the effectiveness and robustness of the proposed method,it is compared with six methods on three datasets,DeepCrack,CFD,and Crack500,and the results show that it has excellent performance,and the F-score value reaches 87.12%.

Oil spills have always been a threat to the marine ecological environment; thus, it is important to identify and divide oil spill areas on the ocean surface into segments after an oil spill accident occurs to protect the marine ecological environment. However, oil spill area segmentation using ordinary optical images is greatly interfered with by the absorption of light by the deep sea and the distribution of algal organisms on the ocean surface, and it is difficult to improve segmentation accuracy. To address the above problems, a hyperspectral ocean oil spill image segmentation model with multiscale feature fusion (MFFHOSS-Net) is proposed. Specifically, the oil spill segmentation dataset was created using hyperspectral image data from NASA for the Gulf of Mexico oil spill, small-size images after the waveband filtering of the hyperspectral images were generated and the oil spill images were annotated. The model makes full use of having different layers with different characteristics by fusing feature maps of different scales. In addition, an attention mechanism was used to effectively fuse these features to improve the oil spill region segmentation accuracy. A case study, ablation experiments and model evaluation were also carried out in this work. Compared with other models, our proposed method achieved good results according to various evaluation metrics. [ABSTRACT FROM AUTHOR]

Oil spills have always been a threat to the marine ecological environment; thus, it is important to identify and divide oil spill areas on the ocean surface into segments after an oil spill accident occurs to protect the marine ecological environment. However, oil spill area segmentation using ordinary optical images is greatly interfered with by the absorption of light by the deep sea and the distribution of algal organisms on the ocean surface, and it is difficult to improve segmentation accuracy. To address the above problems, a hyperspectral ocean oil spill image segmentation model with multiscale feature fusion (MFFHOSS-Net) is proposed. Specifically, the oil spill segmentation dataset was created using hyperspectral image data from NASA for the Gulf of Mexico oil spill, small-size images after the waveband filtering of the hyperspectral images were generated and the oil spill images were annotated. The model makes full use of having different layers with different characteristics by fusing feature maps of different scales. In addition, an attention mechanism was used to effectively fuse these features to improve the oil spill region segmentation accuracy. A case study, ablation experiments and model evaluation were also carried out in this work. Compared with other models, our proposed method achieved good results according to various evaluation metrics.

Coal petrography extraction is crucial for the accurate analysis of coal reaction characteristics in coal gasification, coal coking, and metal smelting. Nevertheless, automatic extraction remains a challenging task because of the grayscale overlap between exinite and background regions in coal photomicrographs. Inspired by the excellent performance of neural networks in the image segmentation field, this study proposes a reliable coal petrography extraction method that achieves precise segmentation of coal petrography from the background regions. This method uses a novel semantic segmentation model based on Unet, referred to as M2AR-Unet. To improve the efficiency of network learning, the proposed M2AR-Unet framework takes Unet as a baseline and further optimizes the network structure in four ways, namely, an improved residual block composed of four units, a mixed attention module containing multiple attention mechanisms, an edge feature enhancement strategy, and a multiscale feature extraction module composed of a feature pyramid and atrous spatial pyramid pooling module. Compared to current state-of-the-art segmentation network models, the proposed M2AR-Unet offers improved coal petrography extraction integrity and edge extraction. [ABSTRACT FROM AUTHOR]

Underwater images often suffer from color distortion, blurring and low contrast, which is caused by the propagation of light in the underwater environment being affected by the two processes: absorption and scattering. To cope with the poor quality of underwater images, this paper proposes a multiscale fusion underwater image enhancement method based on channel attention mechanism and local binary pattern (LBP). The network consists of three modules: feature aggregation, image reconstruction and LBP enhancement. The feature aggregation module aggregates feature information at different scales of the image, and the image reconstruction module restores the output features to high-quality underwater images. The network also introduces channel attention mechanism to make the network pay more attention to the channels containing important information. The detail information is protected by real-time superposition with feature information. Experimental results demonstrate that the method in this paper produces results with correct colors and complete details, and outperforms existing methods in quantitative metrics. [ABSTRACT FROM AUTHOR]

Class imbalance is a challenge in image segmentation. Deep learning has demonstrated potential advantages as a promising approach in solving such challenging problems because of its powerful ability to extract features. To treat class imbalance in medical image segmentation effectively, in this study, deep learning technology is employed. Further, a new approach, referred to as multiscale fused network with additive channel–spatial attention (MSF-ACSA), is developed. In particular, using the U-shape convolutional network as the structure of the main body network, an additive channel–spatial attention (ACSA) module, which exploits high-level features to guide low-level features for optimal responses, is designed. Therefore, the proposed MSF-ACSA can availably focus on regions of interest while adaptively recalibrating the channel significance of feature maps. Simultaneously, a multiscale fusion with deep supervision module is introduced to incorporate fine-grained details with coarse-grained semantics from diverse scale features. Furthermore, a focal weighted Tversky loss function is proposed to mitigate class imbalance issues. Numerous experimental results indicate that the proposed MSF-ACSA achieves the best performance when compared with state-of-the-art approaches for medical image segmentation. [ABSTRACT FROM AUTHOR]