## 3 Use Case: (Com)2 INet for Levee Piping Hazard Inspection via Remote Sensing
As shown in Figure 7, the Pajiang River is a tributary of the Beijing River. Its upstream area contains the largest comprehensive water conservancy project in Guangdong Province--the Feilai Gorge Reservoir. Downstream, there is the critical protective barrier for the Guangzhou  
Figure 6: Eight frames of thermal infrared imagery with piping targets. metropolitan area--the Beijinga Levee. During the flood season, on one hand, the release of floodwater from the upstream reservoir causes the water level of the Pajiang River to rise significantly due to the backwater effect of the Beijing River's floodwaters, posing a threat to the safety of the levees. On the other hand, in order to protect the downstream levee, the Pajiang River basin has to be used as a flood storage and detention area. Therefore, the safety of the levee here not only affects both banks of the Pajiang River but may also impact the safety of the Guangzhou metropolitan area. Comparison in accuracy of target extractionAs shown in Table 1, SACM demonstrates a significant advantage in terms of target extraction accuracy. As indicated by the data of Table 1, SACM consistently achieves the highest mIoU values across all tests, with an average mIoU of 0.1469. This suggests that SACM provides superior precision in extracting piping hazards. In contrast, traditional algorithms such as KM, GMM, and FCM exhibit lower average mIoU values of 0.0423, 0.0269, and 0.0303, respectively. These conventional methods often struggle with the complexities of thermal infrared remote sensing imagery, where background noise and environmental variations can adversely affect target extraction accuracy. SACM effectively mitigates irrelevant background regions through its progressive adaptive clustering strategy, enhancing the precision of piping hazard extraction. Its robust and reliable performance in complex environments is evident, maintaining stability under varying weather conditions. Consequently, SACM demonstrates superior performance in piping detection tasks, validating its applicability and efficacy in challenging scenarios. Comparison in computational timeAs shown in Table 2, SC-based SACM also exhibits a remarkable advantage in computational efficiency. SC-based SACM achieves the lowest average runtime per frame at 0.2288 s, significantly outperforming other algorithms. This indicates that SC-based SACM is highly efficient in processing thermal infrared imagery for piping hazard detection. In contrast, traditional monolithic-based algorithms, such as KM, GMM, and FCM, have average runtimes of 6.7628, 96.3845, and 209.3182 s per frame, respectively. To be specific, the time complexity of monolithic-based KM is \(O\big{(}k(n-k)^{2}\big{)}\), where \(n\) indicates the number of data points and \(k\) indicates the number of clusters. The time complexity of monolithic-based GMM is \(O(nkD^{2})\), where \(D\) indicates the dimension of the covariance matrix. The time complexity of monolithic-based FCM is \(O(nkcl)\), where \(c\) indicates the number of iterations and \(I\) indicates the dimension of  
Figure 7: Study area and levee pipings. data. Since FCM needs to process fuzzy membership, the calculation process is relatively complicated and usually requires multiple iterations to converge, so the computational time is higher. The efficiency of SC-based SACM is attributed to the fact that the model can be split and coordinated among multiple computing devices, and its streamlined clustering process, which reduces computational overhead while maintaining higher accuracy. This efficiency makes SC-based SACM particularly suitable for real-time applications, where rapid processing is crucial. The significant reduction in runtime not only demonstrates the computational superiority of SC-based SACM but also its potential for large-scale deployment in practical scenarios. To sum up, SACM integrates adaptive background removal and cluster number selection using the elbow method, which is specifically tailored for levee piping hazard identification in UAV thermal infrared imagery.