Automatic Pavement Object Detection Using Superpixel Segmentation Combined With Conditional Random Field

Sultani, Waqas; Mokhtari, Soroush; Yun, Hae‐Bum

doi:10.1109/tits.2017.2728680

Cited by 34 publications

(23 citation statements)

References 28 publications

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“…Particularly, artificial markers or specific objects (e.g. lane marker, manhole and patches on roadways) on the asset were utilized to clarify novel methods in [52].…”

Section: 24mentioning

confidence: 99%

Computer Vision Techniques in Construction, Operation and Maintenance Phases of Civil Assets: A Critical Review

Xu¹,

Wang²,

Wang³

et al. 2019

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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Throughout the life cycle of civil assets, construction, operation and maintenance phases require monitoring to assure reasonable decision makings. Current methods always involve speciallyassigned personnel conducting on-site inspections, which are work-intensive, time-consuming and errorprone. Computer vision, as a powerful alternative to manual inspection, has been extensively studied during the past decades. On the basis of existing summary papers, this paper reviews a wide range of literatures, including journal articles, conference proceedings and other resources. Current applications of computer vision during construction, operation and maintenance stages of civil structures are concluded, with a special focus on operation and maintenance phase. This review aims to provide a comprehensive insight about the utilization of computer vision in civil engineering and an inspiring guidance for future research.

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“…Particularly, artificial markers or specific objects (e.g. lane marker, manhole and patches on roadways) on the asset were utilized to clarify novel methods in [52].…”

Section: 24mentioning

confidence: 99%

Computer Vision Techniques in Construction, Operation and Maintenance Phases of Civil Assets: A Critical Review

Xu¹,

Wang²,

Wang³

et al. 2019

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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“…Hence, the quality of the feature descriptors has a significant impact on the performance of CRF-based segmentation models. In order to further accelerate the segmentation speed for large-scale SAR images, the input generally is over-segmented into superpixels [19,20]. However, effective feature extraction algorithms for superpixels remain a challenge.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the previous methods focus on hand-crafted features for superpixels, e.g., histogram of oriented gradients (HOG) [21], gray-level co-occurrence matrix (GLCM) [22], and co-occurrence matrix (COOC) [23]. These hand-crafted features include many coefficients determined by previous knowledge and experiences in practice [19], which is difficult and time-consuming. Besides, the speckle noise existing in SAR images also increases the difficulty of feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Weakly Supervised Segmentation of SAR Imagery Using Superpixel and Hierarchically Adversarial CRF

Gao

Sun

et al. 2019

Remote Sensing

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Synthetic aperture radar (SAR) image segmentation aims at generating homogeneous regions from a pixel-based image and is the basis of image interpretation. However, most of the existing segmentation methods usually neglect the appearance and spatial consistency during feature extraction and also require a large number of training data. In addition, pixel-based processing cannot meet the real time requirement. We hereby present a weakly supervised algorithm to perform the task of segmentation for high-resolution SAR images. For effective segmentation, the input image is first over-segmented into a set of primitive superpixels. This algorithm combines hierarchical conditional generative adversarial nets (CGAN) and conditional random fields (CRF). The CGAN-based networks can leverage abundant unlabeled data learning parameters, reducing their reliance on the labeled samples. In order to preserve neighborhood consistency in the feature extraction stage, the hierarchical CGAN is composed of two sub-networks, which are employed to extract the information of the central superpixels and the corresponding background superpixels, respectively. Afterwards, CRF is utilized to perform label optimization using the concatenated features. Quantified experiments on an airborne SAR image dataset prove that the proposed method can effectively learn feature representations and achieve competitive accuracy to the state-of-the-art segmentation approaches. More specifically, our algorithm has a higher Cohen's kappa coefficient and overall accuracy. Its computation time is less than the current mainstream pixel-level semantic segmentation networks.

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“…Background modeling methods (such as [5][6][7]) can achieve automatic segmentation of objects and backgrounds, but the establishment and update of models is time-consuming and dynamic backgrounds will interfere with the results. Threshold segmentation methods (such as [8,9]) are convenient and efficient for situations with simple backgrounds and prominent objects, but the detection effect under complex circumstances is not satisfactory. To sum up, traditional detection algorithms have limitations, which make it difficult to meet the needs of complex and diverse scenarios in real life.…”

Section: Introductionmentioning

confidence: 99%

Military Object Real-Time Detection Technology Combined with Visual Salience and Psychology

Hua

Wang

et al. 2018

Electronics

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This paper presents a method of military object detection through the combination of human visual salience and visual psychology, so as to achieve rapid and accurate detection of military objects on the vast and complex battlefield. Inspired by the process of human visual information processing, this paper establishes a salient region detection model based on double channel and feature fusion. In this model the pre-attention channel is to process information on the position and contrast of images, and the sub-attention channel is to integrate information on primary visual features first and then merges results of the two channels to determine the salient region. The main theory of Gestalt visual psychology is then used as the constraint condition to integrate the candidate salient regions and to obtain the object figure with overall perception. After that, the efficient sub-window search method is used to detect and filter the object in order to determine the location and range of objects. The experimental results show that, when compared with the existing algorithms, the algorithm proposed in this paper has prominent advantages in precision, effectiveness, and simplicity, which not only significantly reduces the effectiveness of battlefield camouflage and deception but also achieves the rapid and accurate detection of military objects, thus promoting its application prospect.

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Automatic Pavement Object Detection Using Superpixel Segmentation Combined With Conditional Random Field

Cited by 34 publications

References 28 publications

Computer Vision Techniques in Construction, Operation and Maintenance Phases of Civil Assets: A Critical Review

Computer Vision Techniques in Construction, Operation and Maintenance Phases of Civil Assets: A Critical Review

Weakly Supervised Segmentation of SAR Imagery Using Superpixel and Hierarchically Adversarial CRF

Military Object Real-Time Detection Technology Combined with Visual Salience and Psychology

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