Application of Image Fusion Algorithm Based on Convolutional Neural Network in Power Inspection
In order to effectively solve the problems of difficult,high risk factor and low efficiency of manual inspection opera-tions of cable and power generation equipment,this paper combines the key technologies of power inspection drones and video processing,and proposes an abnormal state recognition algorithm for power equipment based on the combination of improved convolutional neural network and image feature fusion processing technology,and applies it to the inspection of power genera-tion equipment and cables.The technique uses image fusion technology to propose redundant information and retain classifica-tion feature terms after image matching and feature processing.Further,video compression technology is used to construct the UAV inspection video analysis model as the training sample of convolutional neural network model to identify the abnormal state of equipment for the video data after key data refinement.Meanwhile,the cross-entropy function is used to optimize the convolutional neural network model and improve the classification effect.The proposed algorithm is compared with other image recognition algorithms,and the results show that the accuracy and processing performance of the proposed algorithm are the best and have certain application promotion value.
power inspectionconvolutional neural networkimage recognitionfeature fusion
万方数据
维普
