# CPDet: Circle-Permutation-Aware Object Detection for Heat Exchanger Cleaning
## 2 Related Work
### Specific Industrial Scenarios Detector  
Current deep learning-based object detection methods are widely applied across various fields [21; 22], and the application of object detection technology to industries such as manufacturing, medicine, and artificial materials has further propelled the development of these fields. It is worth noting that the ease of deployment and extendability of YOLOv5 [20] facilitate its wide application in industrial scenarios. To adapt the model to the detection requirements of specific industrial scenarios, [23; 24] demonstrate methods that are designed by introducing specific modules to meet the industrial detection. [PERSON]. [25] introduced the transformer mechanism, using the Swin Transformer for feature extraction to capture comprehensive image features, thereby enhancing the detection capability for incomplete objects [26]. Using the improved Canny-Devernay sub-pixel edge detection algorithm in combination with the YOLOv6 deep learning network for rapid and accurate detection of surface defects on metal workpieces was conducted in [27]. Based on YOLOv7, the GhostModule has been introduced to enhance real-time performance, while SE blocks and the EIoU loss function have been incorporated to highlight important information in the image and further accelerate the model's convergence speed for the purpose of detecting whether on-site construction workers are wearing safety helmets. Ref. [28] added small object detection layers and streamlined large object detection layers to enhance the effectiveness of data manipulation while integrating the EMA mechanism into the C2 fmodule to strengthen feature perception capabilities, and this approach better achieved helmet-wearing detection. Tph-yolov5 [11] addresses the problem of small-object omission by increasing the prediction of head handling of large-scale variances and integrating Transformer Prediction Heads (TPHs) into YOLOv5. UTD-Yolov5 [29] improves object detection accuracy by replacing the original backbone with a two-stage cascaded CSP (CSP2) and introducing the visual channel attention mechanism module SE [30] to solve feature extraction issues in blurry images. Light-YOLOv5 [31] enhances the interaction between the backbone network and global information, as well as the extraction of flame and smoke features, by incorporating separable transformation convolution modules. In addition, the algorithm utilizes the Mish activation function and SloU loss to improve convergence speed and accuracy. Swin-Transformer-YOLOv5 [32] proposes an integrated grape detection model that combines Swin Transformer and YOLOv5 for real-time object detection of grape clusters. MSFT-YOLO [33] achieves the organic integration of features and global information by incorporating Transformer-based modules in the backbone and detection head. It also improves the detector's ability to dynamically adjust to objects of different scales by combining multi-scale feature fusion structures. AFF-YOLO [34] integrates an effective channel attention network (ECA-Net) in the neck of the network to enhance attention and filtering. It focuses on relevant steel surface defects and introduces bidirectional information flow through a cascaded BiFPN [35]. The Adaptive Spatial Feature Fusion (ASFF) module has been added to the prediction head to enhance feature fusion at different scales, allowing the model to better learn and recognize steel defects. The above methods prove that the performance of the detector can be enhanced by analyzing the specific demands of industrial scenarios and the characteristics of the dataset. Inspired by these methods, this study analyzed data regarding the heat exchanger and designed corresponding modules to enhance the detection performance of the base model for circle detection.