Improved YOLOv5s-Based Crack Detection Method for Sealant-Spraying Devices

The manual spraying of sealant on train side doors is associated with high costs and significant safety risks. To address this challenge, this study proposes an automated crack localization method for sealant-spraying devices by enhancing the YOLOv5s network, with a specific focus on leveraging principles of symmetry. First, an automated sealant-spraying device is designed for operation and data acquisition. Geometric symmetry is then exploited through Zhang’s camera calibration method to accurately establish the two-dimensional mapping between spatial coordinates and the image plane, a process fundamental to spatial reasoning. The core of our approach lies in introducing structural and computational symmetry into the deep learning model. The original YOLOv5s network is improved by integrating the Selective Context Convolutional module and the Skew Intersection over Union (IoU) Loss function, which streamline computation and boost detection accuracy. Furthermore, we replace the standard C3 module with an improved version that incorporates a Reparameterization Visual Transfer block, enhancing feature representation through structural re-parameterization symmetry between training and inference phases. Validation using data from a coal handling facility demonstrates that the improved YOLOv5s model achieves superior performance in precision, mAP@0.5, and recall compared to the original. The results underscore the critical role of geometric and architectural symmetry in developing robust and efficient vision systems for industrial automation.