A morphological approach to pipe image interpretation based on segmentation by support vector machine

Mashford, John; Rahilly, Mike; Davis, Paul F.; Burn, Stewart

doi:10.1016/j.autcon.2010.06.001

Cited by 48 publications

(19 citation statements)

References 13 publications

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“…Researchers try to improve CCTV performance by combining it with one or more sensors. Recent studies have proposed innovative algorithms to automate sewer pipe inspection by the above-mentioned hybrid techniques like SSET [19][20][21][22][23][24], introduced as a superior alternative to CCTV for accurate condition assessment [25]. Meanwhile, other algorithms proposed for automating other inspection technologies such as Laser profiler and CCTV [26] and sonar techniques to provide enhanced 3D graphs which represent the inner sewer surfaces [27].…”

Section: Sewer Inspection Technologies Comparisonmentioning

confidence: 99%

Review on Computer Aided Sewer Pipeline Defect Detection and Condition Assessment

2019

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Physical and operational inspection of sewer pipelines is critical to sustaining an acceptable level of system serviceability. Emerging inspection tools in addition to developments in sensor and lens technologies have facilitated sewer condition assessment and increased the quality and consistency of provided data. Meanwhile, sewer networks are too vast to be adequately investigated manually so the development of innovative computer vision techniques for automation applications has become an interest point of recent studies. This review paper presents the current state of inspection technology practices in sewer pipelines. An overall inspection tool comparison was conducted and the advantages and disadvantages of each method were discussed. This was followed by a comprehensive review of recent studies on visual inspection automation using computer vision and machine learning techniques. Finally, current achievements and limitations of existing automation methods were debated to outline open challenges and future research for both infrastructure management and computer science researchers.

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Section: Sewer Inspection Technologies Comparisonmentioning

confidence: 99%

Review on Computer Aided Sewer Pipeline Defect Detection and Condition Assessment

2019

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“…[6][7][8][9][10][11][12][13][14][15][16] To overcome the limitations diagnosed by human interpretation on CCTV inspection images, the main alternative automated technologies applicable to pipeline inspection are based on vision, laser, 9 and ultrasonic. Anomaly detection and condition assessment of pipeline, also known in some literature as underground pipe detection, has been a high concern in a wide variety of applications.…”

Section: Introductionmentioning

confidence: 99%

“…Anomaly detection and condition assessment of pipeline, also known in some literature as underground pipe detection, has been a high concern in a wide variety of applications. 11,12 Moselhi and Shehab-Eldden 13 employ support vector machines (SVM) to detect pipe defects based on geometrical features. 10 Obviously, the methods based on robot vision provide very rich visualized condition information.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, vision-based technology has become the most popular technology for pipeline inspection. [12][13][14][15] Due to natural shape irregularities and the complex imaging environment, there are not the specific textural features or shape features of duct defects for automatic defect survey. Sinha and Fieguth 14 use mathematical morphology methodology for segmentation of pipe defects including crack, hole, joint, collapse pipe, and lateral, and perform neuro-fuzzy network, to classify the pipe defects.…”

Section: Introductionmentioning

confidence: 99%

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Rapid cascade condition assessment of ductwork via robot vision

Wang

2012

Opt. Eng

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Automatic assessment of condition in ductwork is very desirable in applications. Presented is a visual condition diagnosis approach, which is capable of processing images rapidly and achieving high accuracy rates. A hierarchical coarse-to-fine image segmentation method is employed. False alarms could thus be progressively eliminated, which is robust in strongly noisy conditions. The simple classifiers combined in a cascade quickly classify the detected images and discard the uninterested (non-object) images, leaving more computation power on promising object-like regions. The features of each simple classifier are selected based on the Bhattacharyya distance. The cascade can be viewed as an object-specific focus-of-attention mechanism. Experimental results validate the effectiveness and rapidity of the proposed assessment method.

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“…Among all local approaches, mathematical morphological is a theory and technique used for the analysis and processing of geometrical structures [4]. It is most commonly applied to digital images, but can be employed as well on graphs, surface meshes, solids, and many other spatial structures.…”

Section: Introductionmentioning

confidence: 99%

A Fast Document Image Denoising Method based on Packed Binary Format and Source Word Accumulation

Zhang¹,

Wu²

2011

JCIT

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In order to perform document image denoising more efficiently and rapidly, we proposed a hybrid method which used the Otsu's method to initial binary quantization, used two dimensional median filters to reduce the left pepper & salt noises, and performed open and close operators to eliminate the remained noises. Besides, a fast calculation strategy was proposed based on packed binary (PB) format and source word accumulation (SWA). The PB format uses a uint32 word to represent 32 binary pixels, and SWA is capable of directly performing erosion and dilation processes on this format. Experiments demonstrate that the computation time using acceleration is only 18.92% and 18.57% of those not using acceleration for erosion and dilation, respectively. Besides, the denoising method using acceleration is efficient on denoising while keep the quality of thin strokes, and it results in a 17.68% reduction in computation time.

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A morphological approach to pipe image interpretation based on segmentation by support vector machine

Cited by 48 publications

References 13 publications

Review on Computer Aided Sewer Pipeline Defect Detection and Condition Assessment

Review on Computer Aided Sewer Pipeline Defect Detection and Condition Assessment

Rapid cascade condition assessment of ductwork via robot vision

A Fast Document Image Denoising Method based on Packed Binary Format and Source Word Accumulation

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