Night-time surveillance is important for safety and security purposes. For this reason, several studies have attempted to automatically detect people intruding into restricted areas by using infrared cameras. However, detecting people from infrared CCTV (closed-circuit television) is challenging because they are usually installed in overhead locations and people only occupy small regions in the resulting image. Therefore, this study proposes an accurate and efficient method for detecting people in infrared CCTV images during the night-time. For this purpose, three different infrared image datasets were constructed; two obtained from an infrared CCTV installed on a public beach and another obtained from a forward looking infrared (FLIR) camera installed on a pedestrian bridge. Moreover, a convolution neural network (CNN)-based pixel-wise classifier for fine-grained person detection was implemented. The detection performance of the proposed method was compared against five conventional detection methods. The results demonstrate that the proposed CNN-based human detection approach outperforms conventional detection approaches in all datasets. Especially, the proposed method maintained F1 scores of above 80% in object-level detection for all datasets. By improving the performance of human detection from infrared images, we expect that this research will contribute to the safety and security of public areas during night-time.

In South Korea, sewage pipeline exploration devices have been developed using high resolution digital camera of 2 mega-pixels or above. However, most of the devices are less than 300 kilo-pixels due to poverty of the business. Moreover, since 100 kilo-pixels devices are widely used, environment for image processing is very poor. In this study, we adapted very low resolution (240 × 320 = 76,800 pixels) images by which it is difficult to detect cracks. Considering that images of sewers in South Korea have very low resolution, this study selected low resolution images to be investigated. An automatic crack detection technique has been studied using digital image processing technology for low resolution images of sewage pipeline. Authors have developed a program to automatically detect cracks as per eight steps based on MATLAB’s functions. In this study, the third step covers an algorithm developed to find optimal threshold value, and the sixth step deals with algorithm to determine cracks. When we select [1.11:1.29] of zero padding range, exact detection rate is 89.2% and error rate is 4.44%. As the result, in spite of very low-resolution images, the performance of crack detection turned out to be excellent.

A disaster preventive structural health monitoring (SHM) system needs to be equipped with the following abilities: First, it should be able to simultaneously measure diverse types of data (e.g., displacement, velocity, acceleration, strain, load, temperature, humidity, etc.) for accurate diagnosis. Second, it also requires standalone power supply to guarantee its immediate response in crisis (e.g., sudden interruption of normal AC power in disaster situations). Furthermore, it should be capable of prompt processing and realtime wireless communication of a huge amount of data. Therefore, this study is aimed at developing a wireless unified-maintenance system (WUMS) that would satisfy all the requirements for a disaster preventive SHM system of civil structures. The WUMS is designed to measure diverse types of structural responses in realtime based on wireless communication, allowing users to selectively use WiFi RF band and finally working in standalone mode by means of the field-programmable gate array (FPGA) technology. To verify its performance, the following tests were performed: (i) A test to see how far communication is possible in open field, (ii) a test on a shaker to see how accurate responses are, (iii) a modal test on a bridge to see how exactly characteristic real-time dynamic responses are of structures. The test results proved that the WUMS was able to secure stable communication far up to nearly 800 m away by acquiring wireless responses in realtime accurately, when compared to the displacement and acceleration responses which were acquired through wired communication. The analysis of dynamic characteristics also showed that the wireless acceleration responses in real-time represented satisfactorily the dynamic properties of structures. Therefore, the WUMS is proved valid as a SHM, and its outstanding performance is also proven.

Crack detection in structures is an important and time-consuming element of monitoring the health of structures and ensuring structural safety. The traditional visual inspection of structures can be unsafe and may produce inconsistent results. Thus, there is a need for a method to easily and accurately identify and analyze cracks. In this study, algorithms for automatically detecting the size and location of cracks in concrete images were developed. Cracks were automatically detected in a total of 10 steps. In steps 5 and 9, two user algorithms were added to increase crack detection accuracy, where 1000 crack images and 1000 non-crack images were used, respectively. In the crack image, 100% of the cracks were detected, but 95.3% of the results were very good, even if the results that were not bad in terms of quality were excluded. In addition, the accuracy of detecting non-crack images was also very good (96.9%). Thus, it is expected that the crack detection algorithm presented in this study will be able to detect the location and size of cracks in concrete. Moreover, these algorithms will help in observing the soundness of structures and ensuring their safety.

Background: A quantitative model was developed and applied for analyzing the correlation between hydrogen-induced corrosion cracking in both main cable wires and degraded stiffening of the girders of a cable suspension bridge, considering maintenance effects across time and space. Method: Bayesian inference is applied for predicting the correlations among the wires in the main cables owed to hydrogen-induced cracking (HIC) in the cable wires of a steel bridge, by using the improved hierarchical Bayesian models proposed here. Results: The simulated risk prediction under decreased strength of cable wires, due to the corrosion cracking, yields posterior distributions based on prior distributions and likelihoods. The Bayesian inference model can be applied to the design and maintenance of highly corroded and correlated components Data are updated through analyzed information from previous crack steps. A numerical example including not only reliability indices but also probabilities of failure for cable wires, damaged by HIC, is then presented. Compared with a conventional linear prediction model, the one herein developed provides highly improved convergence and closeness to the analyzed data. Conclusion: The proposed model can be used as a diagnostic or prognostic prediction tool for the performance of corroded bridge cable wires with crack propagation, allowing the development of maintenance plans for mechanical components and the overall structural system.

Imaging devices of less than 300,000 pixels are mostly used for sewage conduit exploration due to the petty nature of the survey industry in Korea. Particularly, devices of less than 100,000 pixels are still widely used, and the environment for image processing is very dim. Since the sewage conduit images covered in this study have a very low resolution (240 × 320 = 76,800 pixels), it is very difficult to detect cracks. Because most of the resolutions of the sewer conduit images are very low in Korea, this problem of low resolution was selected as the subject of this study. Cracks were detected through a total of six steps of improving the crack in Step 2, finding the optimal threshold value in Step 3, and applying an algorithm to detect cracks in Step 5. Cracks were effectively detected by the optimal parameters in Steps 2 and 3 and the user algorithm in Step 5. Despite the very low resolution, the cracked images showed a 96.4% accuracy of detection, and the non-cracked images showed 94.5% accuracy. Moreover, the analysis was excellent in quality. It is believed that the findings of this study can be effectively used for crack detection with low-resolution images.