# Computer Vision and Image Processing Approaches for Corrosion Detection
## 4 Corrosion Detection Approaches
### Ground Penetrating Radar  
Ground penetrating radar (GPR) uses electromagnetic reflection to penetrate the surface of materials used for corrosion detection. A short, microscopic electromagnetic energy pulse is transmitted to the surface in the frequency range of 500 MHz to 2.5 GHz. The amplitude-time signal is recorded using the reflected waves of GPR antennae. The advantages of GPR include fast scanning, being sensitive to embedded material in the structure, good quality images of the structure, and greater depth penetration. However, GPR data are quite difficult to interpret, therefore, they require expert knowledge and skilled operators. The deep post-processing of captured images is also required. Moreover, this method must be applied periodically, and it is not capable of monitoring corrosion progression online. These shortcomings are caused by data analysis instead of equipment. Thus, computer vision and image processing approaches can be used to accurately analyze complicated GPR data.  
Researchers in [39] employed a combination of GPR data, ANN, and image processing to identify the degree of corrosion in reinforced steel concrete structures. Edge detection and k-means clustering image processing techniques were used to investigate differences between corrosion features. ANN was used to interpret and extract complicated features data from GPR images. The images were taken in a short period of time to reduce the blurry effect on GPR images. The results showed that k-means clustering managed to extract and partition a corrosion image into multiple segments. The obtained semantic information classified the area into no corrosion, low corrosion, middle corrosion, and high corrosion categories. The accurate edges of multiple segments areas were detected with a Sobel edge detector. Moreover, GPR was used with Fourier transform and a Gaussian filter [40] with 1 GHz frequency to monitor and predict the degree of corrosion. Reinforced steel structures, such as floors, roofs, and concrete walls, were scanned and the obtained images were pre-processed to detect noise and anomalies. The results were validated using simulation and field data tests. This approach yielded the most accurate prediction with regard to the degree of rebar corrosion, making it a superior technique for detecting and identifying corrosion. Moreover, GPR can also be combined with other imaging techniques to further improve corrosion detection accuracy. Acoustic scanning and GPR were deployed in [41] for corrosion detection in straight and curved concrete bridge decks. A frequency range of 0.5 to 5 KHz was used to reconstruct 2D images from the energy map. The results  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Prediction Model** & & **Description** & **Application/Method** \\ \hline Statistical & & \(\bullet\) & Statistical analysis is used to predict & \\  & & and detect the progression of & \(\bullet\) & Markov chain with gray level \\ Statistical & & corrosion using historical data. & co-occurrence method [28]. \\  & & \(\bullet\) & Gathers data from CCTV systems & \(\bullet\) & Polynomial regression [30] and \\  & & installed in various locations such as & & linear regression [31; 32]. \\ \hline Deterministic & & \(\bullet\) & Field experiments involving images & \\  & & and videos are used to examine the & \(\bullet\) & Structural health monitoring and \\  & & connections between the & & digital twin models [34]. \\  & & parameters/variables of & & \(\bullet\) \\  & & corroded materials. & & \\  & \(\bullet\) & Easier to develop. & & \\  & \(\bullet\) & Extrapolation accuracy. & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._showed that by combining both deterioration and acoustic scanning maps, comprehensive corrosion detection can be provided in the bridge decks.