基于YOLOv5的钢结构节点损伤检测研究

Research on Damage Detection of Steel Structure Joints Based on YOLOv5

以钢结构节点损伤检测为出发点,针对算法在个人困难数据集上的优化问题,使用预训练权重,通过分析训练过程中的损失趋势评估合适的训练周期。选择CBAM注意力机制提升迁移学习的效率和性能,使用AdamW优化器加快模型收敛速度,改善数据集的划分策略以展现模型真实性能,提高模型的鲁棒性,防止过拟合。根据先进算法理论优化了模型损失函数,提升模型在个人数据集上的精确率和召回率。针对问题复杂度与算法复杂度匹配性进行试验,选择最适合个人数据集的YOLOv5n6模型,最终优化出适合在现实场景中应用的钢结构节点损伤检测模型权重。

Taking steel structure joints damage detection as the starting point,for the optimization problem of the algorithm on personal difficult datasets,using pre-training weights,evaluating the appropriate training period by analyzing the loss trend during the training process,selecting the CBAM attention mechanism to improve the efficiency and performance of the migration learning,using the AdamW optimizer to accelerate the convergence speed of the model,improving the dataset partitioning strategy to show 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 the accuracy and recall of the model on the personal dataset.The tests were conducted for the problem of the matching between problem complexity and algorithm complexity,select the YOLOv5n6 model that is most suitable for the personal dataset,and ultimately optimize the model weights of steel structure joints damage detection,which is suitable to be applied in the real-world scenarios.