## 3 Results and Discussion
### SVM Classification Experiment Results  
In order to verify the effectiveness of the proposed method, infrared images in the five working conditions of the normal pipeline, leaking pipeline, worn pipeline, normal value and leaking valve are collected, respectively. In each working conditions, 180 frame infrared images were selected to be used as SVM training samples, and 120 frame infrared images were selected to be used as SVM test samples. In addition, the genetic algorithm [31; 32], particle swarm optimization [33; 34] and IFA are used to optimize the two key SVM parameters of the penalty factor C and radial basis kernel function to verify the optimization performance of IFA. The classification recognition results are shown in Tables 9-12.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{5}{c}{**Image Information Entropy**} \\  & **Normal Pipeline** & **Leaking Pipeline** & **Worm Pipeline** & **Normal Valve** & **Leaking Valve** \\ \hline Noisy image & 6.70 & 6.39 & 6.56 & 5.99 & 6.53 \\ Median filtering & 6.55 & 6.25 & 6.42 & 5.82 & 6.39 \\ Bilateral filtering & 7.04 & 6.76 & 6.98 & 6.33 & 6.87 \\ BM3D & 7.92 & 7.69 & 7.88 & 7.34 & 7.77 \\ WTD-FA & 7.08 & 6.82 & 7.02 & 6.38 & 6.92 \\ WTD-FOA & 7.08 & 6.82 & 7.03 & 6.38 & 6.91 \\ WTD-IFA & 7.10 & 6.83 & 7.04 & 6.39 & 6.92 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Information entropy of denoising images.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{5}{c}{**Image Information Entropy**} \\  & **Normal Pipeline** & **Leaking Pipeline** & **Worm Pipeline** & **Normal Valve** & **Leaking Valve** \\ \hline Noisy image & 20.37 & 21.18 & 20.02 & 21.91 & 21.21 \\ Median filtering & 22.96 & 22.81 & 22.74 & 23.86 & 22.95 \\ Bilateral filtering & 25.84 & 24.91 & 25.59 & 25.67 & 24.67 \\ BM3D & 26.34 & 25.95 & 26.00 & 25.84 & 25.84 \\ WTD-FA & 24.04 & 23.39 & 23.97 & 23.96 & 23.52 \\ WTD-FOA & 23.99 & 23.35 & 23.90 & 24.33 & 23.45 \\ WTD-IFA & 25.44 & 24.26 & 24.15 & 24.44 & 24.20 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Energy gradient of denoising images.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{5}{c}{**Image Information Entropy**} \\  & **Normal Pipeline** & **Leaking Pipeline** & **Worm Pipeline** & **Normal Valve** & **Leaking Valve** \\ \hline Median filtering & 1.0989 & 1.0479 & 1.0426 & 1.0575 & 1.0632 \\ Bilateral filtering & 1.8838 & 2.0553 & 2.1209 & 2.0259 & 2.2391 \\ BM3D & 14.7613 & 17.3301 & 17.3533 & 15.0779 & 17.3111 \\ WTD-FA & 1.0848 & 1.1081 & 1.2193 & 1.1766 & 1.2209 \\ WTD-FOA & 1.1856 & 1.2467 & 1.2375 & 1.2531 & 1.2738 \\ WTD-IFA & 1.0839 & 1.1192 & 1.1205 & 1.1784 & 1.3218 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Denoising time of infrared images.  
It can be seen from the Tables 9-12 that the infrared image is easy to be confused under the two working conditions of leakage pipeline and worn pipeline and the image of the leaking valve is easily erroneously recognized as a normal valve image. In the comparison of the above three optimization algorithms, the support vector machine optimized by IFA has the highest classification accuracy of 96.00%, and the average processing classification time of a single image is 2.35 s, which can meet the recognition requirements of the system.