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Power line inspection is an important means to eliminate hidden dangers of power lines. It is a difficult research problem how to solve the low accuracy of power line inspection based on deep neural network (DNN) due to the problems of multi‐view‐shape, small‐size object. In this paper, an automatic detection model based on Feature visual clustering network (FVCNet) for power line inspection is established. First, an unsupervised clustering method for power line inspection is proposed, and applied to construct a detection model which can recognize multi‐view‐shape objects and enhanced object features. Then, the bilinear interpolation method is used to Feature enhancement method, and the enhanced high‐level semantics and low‐level semantics are fused to solve the problems of small object size and single sample. In this paper, FVCNet is applied to the MS‐COCO 2017 data set and self‐made power line inspection data set, and the test accuracy is increased to 61.2% and 82.0%, respectively. Compared with other models, especially for the categories that are greatly affected by multi‐view‐shape, the test accuracy has been improved significantly. [ABSTRACT FROM AUTHOR]

Compute is a term coined from the etymology of French and Latin words computer and computare respectively, so is computing. This field of computing has grown enormously over the years. From the simple, traditional Turing machine invented in 1936 by Alan Turing to the current neural network (NN) computing. NNs, a field of artificial intelligence (AI) was exhilarated from the structure and inner workings of the brain. Just as the brain is, that is, an interconnection of neurons, so is the NN which is an interconnection of basic structures known as the perceptron. They do not differ much in structure. Their only difference is that one is artificial while the other is entirely biological. The hierarchical intricacies of the NN can be represented in three layers: the perceptron, artificial NN (ANN), and deep NN (DNN). With the influx of mental and behavioral disorders, basic surveillance, and the urgency to improve the mental health of people, studying the behavioral dynamics of people is requisite. CCTV and street cameras can only do so much, thus the need to employ the field of NN which makes use of supervised learning in training the models to perfect and automate surveillance. The results of this retrospective research indicate that the use of the NN model surpasses those of traditional methods in terms of efficiency and reliability. [ABSTRACT FROM AUTHOR]