Water Supply Pipeline Burr Data Detection Based on Improved LightGBM by Focal Loss
Addressing the issue of low recall in the detection of burrs in water supply pipelines due to data imbalance,this paper proposes an improved method for detecting pipeline burr data by utilizing the Focal Loss function and integrating it with Light-GBM.Firstly,considering the characteristics of pipeline burr data,neighborhood-related features are constructed.Secondly,the Focal Loss function is introduced into LightGBM to increase the model's weight on hard-to-detect burr samples.Different pa-rameter values for Focal Loss are experimented to balance precision and recall.Finally,different parameter settings for Focal Loss are selected for model fusion to further improve the detection performance of the model on imbalanced burr data.Experi-ments are carried out on a real dataset from a municipal water supply pipeline.The experimental results show that,compared with a single model based on the cross-entropy loss function,the fused model with the improved Focal Loss in this paper achieves 33.3 percentage points increase in recall and 18 percentage points increase in F1 score for burr data.However,the pre-cision of burr data detection still needs further improvement.The method proposed in this paper starts with loss function and dy-namically adjusts the weights of difficult and easy samples to effectively improve the detection performance of burr data under un-balanced data.
anomaly detectionFocal LossLightGBMimbalanced databurr data
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