Research on Damage Detection of Steel StructureJoints Based on YOLOv5
About the author:
Institute of Engineering Mechanics, China Earthquake Administration, Harbin, Heilongjiang 150086, China
Abstract:
Taking steel structure joints damage detection as the starting point, for the optimization problemof the algorithm on personal difficult datasets, using pre?training weights, evaluating the appropriatetraining period by analyzing the loss trend during the training process, selecting the CBAM attentionmechanism to improve the efficiency and performance of the migration learning, using the AdamWoptimizer to accelerate the convergence speed of the model, improving the dataset partitioning strategy toshow the real performance of the model, and improve the robustness of the model to prevent overfitting.The model loss function is optimized according to the theory of advanced algorithms to improve theaccuracy and recall of the model on the personal dataset. The tests were conducted for the problem of thematching between problem complexity and algorithm complexity, select the YOLOv5n6 model that is mostsuitable for the personal dataset, and ultimately optimize the model weights of steel structure jointsdamage detection,which is suitable to be applied in the real?world scenarios.
