| dc.description.abstract | This study addresses the challenges in industrial pipeline rust detection by proposing an innovative solution that combines data augmentation techniques with a bidirectional reinforcement learning mechanism. The goal is to enhance the precision, stability, and generalization capability of detection models to handle diverse and complex rust charac-teristics in industrial environments.
For data augmentation, this study utilizes Diffusion Models and ControlNet tech-nology to generate high-resolution rust images. A customized filtering module is integrat-ed to select and optimize features based on specified rust levels. This approach signifi-cantly expands the diversity of the training dataset and effectively resolves the issue of insufficient samples in the original dataset. By combining generated images with re-al-world captured images, the model demonstrates exceptional performance in detecting small, irregular, and complex rusted regions. Experimental results show that with this data augmentation technique, the detection accuracy of YOLOv8 and YOLOv11 models im-proved significantly, with a notable reduction in missed detections, particularly in irregular shapes and highly noisy backgrounds.
In addition, this study introduces a bidirectional reinforcement learning mechanism that dynamically optimizes model strategies through reward and penalty feedback. During the initial training phase, human intervention plays a critical role in correcting model de-tection results, helping the model focus on rust areas. As training progresses, the bidirec-tional reinforcement learning system gradually replaces human intervention by adaptively adjusting detection strategies. This enhancement strengthens the model’s adaptability to diverse industrial scenarios while significantly improving detection stability and accuracy. In summary, this study successfully integrates data augmentation techniques with a bidi-rectional reinforcement learning mechanism, substantially enhancing the overall perfor-mance of industrial rust detection models. It demonstrates the innovative potential and practical value of this combined approach in addressing complex rust detection tasks. | en_US |