A texture‐Based Video Processing Methodology Using Bayesian Data Fusion for Autonomous Crack Detection on Metallic Surfaces

Abstract

Regular inspection of the components of nuclear power plants is important to improve their resilience. However, current inspection practices are time consuming, tedious, and subjective: they involve an operator manually locating cracks in metallic surfaces in the plant by watching videos. At the same time, prevalent automatic crack detection algorithms may not detect cracks in metallic surfaces because these are typically very small and have low contrast. Moreover, the existences of scratches, welds, and grind marks lead to a large number of false positives when state-of-the-art visionbased crack detection algorithms are used. In this study, a novel crack detection approach is proposed based on local binary patterns (LBP), support vector machine (SVM), and Bayesian decision theory. The proposed method aggregates the information obtained from different video frames to enhance the robustness and reliability of detection. The performance of the proposed approach is assessed by using several inspection videos. The results indicate that it is accurate and robust in cases where state-of-the-art crack detection approaches fail. The experiments show that hit rate by 20% and the hit rate achieves 85% with only one false positive per frame. C 2017 Computer-Aided Civil and Infrastructure Engineering.

Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images

“…In particular, we examine whether we can detect eight classes of road damage by applying state‐of‐the‐art object detection methods (discussed in Section 2.4) with the newly created road damage data set (explained in Section 3). Although many excellent methods have been proposed, such as segmentation of cracks on concrete surfaces (O'Byrne et al., ; Nishikawa et al., ) and metallic surfaces (Chen et al., ), our research uses an object detection method. Indeed, although there is research that uses deep learning to evaluate the stability of structures using sensor data (Rafiei and Adeli, , ; Lin et al., ; Rafiei et al., ), in this article, we concentrate on detecting road surface damage using image processing.…”

Unified Vision‐Based Methodology for Simultaneous Concrete Defect Detection and Geolocalization

“…LBP was adopted in the work of Chen et al. () in combination with SVM and Bayesian decision theory to detect cracks on underwater metallic surfaces in nuclear inspection videos.…”

“…However, currently routine monitoring is typically performed by humans through visual inspection (Yeum and Dyke, ), which is usually labor‐intensive, time‐consuming, and even dangerous (Huang et al., ; Butcher et al., ; Adhikari et al., ). To tackle these issues, many approaches have been proposed to automate the visual inspection of concrete defects (CDs) and some of them have been delivered to real‐world applications (Nishikawa et al., ; Huang et al., ; O'Byrne et al., ; Yeum and Dyke, ; Chen et al., ). However, most of the existing works generally rely on exploiting handcrafted low‐level features and traditional machine learning algorithms, which may not work effectively under real‐world conditions where defects inevitably show up with complex variations in appearance.…”

Computer‐Aided Optimization of Surveillance Cameras Placement on Construction Sites

“…Surveillance camera is nonintrusive to general construction tasks without attaching tag to workers or their proactive equipment (Park et al., ; Rezazadeh Azar and McCabe, ). With the advancement of computer vision techniques on construction sites, cameras become significant in numerous implementations such as (a) detecting and tracking construction‐related entities to calculate productivity (Chi et al., ; Gong and Caldas, ; Brilakis et al., ; Chi and Caldas, , ; Gong et al., ) and avoiding collisions (Chen et al., ; Hamledari et al., ); (b) recognizing working condition and environmental context to monitor construction progress and obtain safety context (Gualdi et al., ; Park and Brilakis, ); (c) tracking workforce and detecting motions to prevent proximity to hazards (Teizer and Vela, ; Yang et al., ) and preventing muscular injuries from awkward postures or ergonomic risks (Ray and Teizer, ; Han and Lee, ; Han et al., ; Yang et al., ); (d) monitoring and identifying damages and quality issues (Salem et al., ; Yeum and Dyke, ; Cha et al., ; Cha et al., ; Kong and Li, ), especially visual cracks (Chen et al., ; Zhang et al., ); (e) inspecting structural conditions in hazardous environment (Zhu et al., ; Oh et al., ; Park et al., ); and (f) reconstructing building models (Fleishman et al., ; Olague and Mohr, ). Based on the diverse applications of surveillance cameras, intelligible information is extracted automatically in real time, offering an effective solution to time‐ and labor‐consuming inspections on construction sites (Mirchandani et al., ).…”