Research on defect detection of transmission line fittings based on improved YOLOv8 and semantic knowledge fusion
To address issues such as the uneven distribution of defect samples among classes and the difficulty in extracting tiny features of defects in the task of detecting defects of transmission line bolts and fixtures,a defect detection method for transmission line bolts and fixtures was proposed based on the improvement of YOLOv8 and semantic knowledge fusion.First,the semantic correlation was established by analyzing the relationship between the defective types of bolt fittings in the data samples and the types of fittings carried by that bolt.Then,the BiFusion and RepBlock modules were introduced into the Neck part of the YOLOv8 model to enhance its feature extraction capability.Second,the Loss function of the weights was corrected using an improved fusion of semantic knowledge,further improving the accuracy of the training model and reducing the occurrence of misdetection.Finally,baseline selection experiments,ablation experiments,hyper-parameter experiments,and comparative experiments were conducted,respectively.The experimental results showed that compared with the Baseline model,the improved YOLOv8 method increased the mean average precision(mAP)by 4.0%and improved the accuracy of the key less sample classes by 24.6%,effectively enhancing the defect detection performance for transmission line bolted fittings.The proposed semantic correlation establishment and semantic knowledge fusion method also demonstrated a certain degree of generalizability,providing new methodological support for UAV-based intelligent inspection of transmission lines.
UAV inspectiontransmission line fittingsbolt defect detectionsemantic information fusionYOLOv8
NETL
NSTL
万方数据
