A real‐time lane detection network using two‐directional separation attention

Zhang, Lu; Jiang, Fengling; Yang, Jing; Kong, Bin; Hussain, Amir

doi:10.1111/mice.13051

Cited by 6 publications

(3 citation statements)

References 51 publications

(71 reference statements)

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“…Recent researchers proposed EfficientNet and incorporated attention mechanisms (Chen & He, 2022;Y. Pan & Zhang, 2022;L. Zhang et al, 2023), separable convolution (Zhu et al, 2023;Zou et al, 2022), deformable convolution (Lei et al, 2023), atrous convolution (Siriborvornratanakul, 2023), and other strategies (Zheng et al, 2022) to further enhance model performance.…”

Section: Introductionmentioning

confidence: 99%

“…Novel networks like Faster Region‐CNN (Faster RCNN) and You Only Look Once (YOLO) series (Chun et al., 2023; Z. Zhou et al., 2022) have emerged for object detection, while Mask RCNN and U‐Net (Yamaguchi & Mizutani, 2023) are employed for segmentation. Recent researchers proposed EfficientNet and incorporated attention mechanisms (Chen & He, 2022; Y. Pan & Zhang, 2022; L. Zhang et al., 2023), separable convolution (Zhu et al., 2023; Zou et al., 2022), deformable convolution (Lei et al., 2023), atrous convolution (Siriborvornratanakul, 2023), and other strategies (Zheng et al., 2022) to further enhance model performance.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning‐based automatic classification of three‐level surface information in bridge inspection

Zhang,

Shen,

Lin

et al. 2023

Computer aided Civil Eng

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Bridge inspection ensures that in‐service bridges are managed and maintained in conformity. To enhance the accuracy and efficiency of bridge inspection, an automatic hierarchical model is proposed, which enables the classification and correlation of bridge surface images at three levels, namely, at the structure, component, and defect type level. Thus, the impact of both the defect types and the affected components on bridge safety can be simultaneously considered. The proposed model uses a group of sub‐models instead of the common flat network to realize the multiple tasks, which is advantageous in accuracy, training simplicity, and scalability. The classification accuracy of the hierarchical model in three levels has reached 96%, 92%, and 81%. Results demonstrate the effectiveness of the proposed method in the classification of multi‐scale targets. This study may provide a new strategy for developing a systematic and easily adaptable detection framework for practical bridge engineering.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep learning‐based automatic classification of three‐level surface information in bridge inspection

Zhang,

Shen,

Lin

et al. 2023

Computer aided Civil Eng

View full text Add to dashboard Cite

show abstract

“…Consequently, researchers have started integrating various cutting-edge methods to optimize the performance of segmentation models. In light of the requirement for both semantic understanding and fine-grained detail in segmentation tasks, a suite of attention-based methodologies [ 64 , 65 ] have been developed. These methods are designed to assimilate multi-scale and global contextual information, thereby enhancing the accuracy of defect identification.…”

Section: Introductionmentioning

confidence: 99%

A Binocular Vision-Based Crack Detection and Measurement Method Incorporating Semantic Segmentation

Zhang,

Shen,

Liu

et al. 2023

Sensors

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The morphological characteristics of a crack serve as crucial indicators for rating the condition of the concrete bridge components. Previous studies have predominantly employed deep learning techniques for pixel-level crack detection, while occasionally incorporating monocular devices to quantify the crack dimensions. However, the practical implementation of such methods with the assistance of robots or unmanned aerial vehicles (UAVs) is severely hindered due to their restrictions in frontal image acquisition at known distances. To explore a non-contact inspection approach with enhanced flexibility, efficiency and accuracy, a binocular stereo vision-based method incorporating full convolutional network (FCN) is proposed for detecting and measuring cracks. Firstly, our FCN leverages the benefits of the encoder–decoder architecture to enable precise crack segmentation while simultaneously emphasizing edge details at a rate of approximately four pictures per second in a database that is dominated by complex background cracks. The training results demonstrate a precision of 83.85%, a recall of 85.74% and an F1 score of 84.14%. Secondly, the utilization of binocular stereo vision improves the shooting flexibility and streamlines the image acquisition process. Furthermore, the introduction of a central projection scheme achieves reliable three-dimensional (3D) reconstruction of the crack morphology, effectively avoiding mismatches between the two views and providing more comprehensive dimensional depiction for cracks. An experimental test is also conducted on cracked concrete specimens, where the relative measurement error in crack width ranges from −3.9% to 36.0%, indicating the practical feasibility of our proposed method.

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Attention‐optimized 3D segmentation and reconstruction system for sewer pipelines employing multi‐view images

Ma,

Wang,

Fang

et al. 2024

Computer aided Civil Eng

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Existing deep learning‐based defect inspection results on images lack depth information to fully demonstrate the sewer, despite their high accuracy. To address this limitation, a novel attention‐optimized three‐dimensional (3D) segmentation and reconstruction system for sewer pipelines is presented. First, a real‐time sewer segmentation method called AM‐Pipe‐SegNet is developed to inspect defects (i.e., misalignment, obstacle, and fracture) efficiently. Attention mechanisms (AMs) are introduced to improve the performance of segmentation. Second, an attention‐optimized and sparse‐initialized depth estimation network called AM‐Pipe‐DepNet is presented to generate depth maps from multi‐view images. Third, a 2D‐to‐3D mapping algorithm is proposed to remove noise and transform the sewer segmentation results into 3D spaces. Comparison experiments reveal that incorporating AMs into the network significantly enhances pipe segmentation and 3D reconstruction performance. Finally, two digital replicas of real sewer pipes are built based on photos taken by probes, providing valuable insights for sewer maintenance.

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A real‐time lane detection network using two‐directional separation attention

Cited by 6 publications

References 51 publications

Deep learning‐based automatic classification of three‐level surface information in bridge inspection

Deep learning‐based automatic classification of three‐level surface information in bridge inspection

A Binocular Vision-Based Crack Detection and Measurement Method Incorporating Semantic Segmentation

Attention‐optimized 3D segmentation and reconstruction system for sewer pipelines employing multi‐view images

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