# CPDet: Circle-Permutation-Aware Object Detection for Heat Exchanger Cleaning
## 1 Introduction  
Heat exchangers, known for their necessity in industry, are widely used in almost all large industrial scenarios. Among various heat exchangers, the shell-hole one has reached the largest market share at 49.8% [1]. As a long-standing problem, fouling affects heat exchange efficiency and even disrupts the entire circulation system, resulting in huge energy waste and economic losses. Therefore, regular cleaning of heat exchangers is crucial. Current exchanger cleaning is mainly done manually hole by hole, which is inefficient and time-consuming for large-scale industrial scenarios. Automated cleaning has become an industry trend, and the core of automated cleaning is object detection. Current object detection methods are mainly divided into two-stage and one-stage detection. The two-stage methods [2, 3] involve two separate regressions to generate the final output, resulting in low inference speed. One-stage methods [4, 5] employ a single regression to produce results. This sample and fast pipeline is more suitable for deployment on various hardware platforms. Therefore, one-stage object detection methods are commonly used in industrial detection scenarios, such as weld spot location in industrial welding [6, 7] and the localization of leak positions [8, 9]. However, when applying one-stage methods tospecific industrial scenarios, the lack of model tuning and customization for these special applications can lead to poor detection performance. For example, in defect detection, the contrast between the defect area and the background is low, and the defect scale varies greatly, resulting in poor localization and classification results. To address this issue, MD-YOLO [10] adopts extra data augmentation and multi-channel fusion modules, significantly enhancing the feature extraction capabilities. Similarly, the variation in object scales captured by drones at different altitudes leads to inaccurate localization. Tph-yolov5 [11] solves this problem by adding custom prediction heads and integrating attention modules. Therefore, enhancing detector adaptability in specific scenarios is critical. This study analysis reveals exploitable prior information of holes, as shown in Figure 1, the circle holes are regularly arranged along the horizontal and the vertical. In addition, the intervals of these regularly distributed holes remain consistent. The general methods often mistake reflective spots in the background information for circle holes when detecting circle holes in the dataset, and at the same time, some edge circle holes have weakened feature information due to aging and corrosion. However, the arrangement and distribution of these circle holes have strong regularity. We can apply this prior information to weaken the background features and strengthen the foreground features, thereby reducing the occurrence of missed detections and false detections. Based on this prior idea, this work designed an object detector for detecting the holes of shell-tube heat exchangers, known as the Circle-Permutation-Aware Detector (CPDet), which uses a one-stage detector as the backbone to innovatively extract the prior information of objects, as well as craftily utilizes the prior information to preliminary distinguish the foreground and background areas, with the weighted approach used to enhance the foreground area features and weaken the background area features to achieve guided feature extraction. Specifically, CPDet contains three novel modules: interval prior extraction (IPE), interval prior fusion (IPF), and prior-guided sparse attention (PSA). IPE extracts interval information among holes and then normalizes it to produce prior interval information for computation in the subsequent IPF module. IPF utilizes the prior interval information with a slicing mechanism to divide the original image into a circle domain map and a background domain map.