Transmission line target detection based on improved deep learning
Aiming at the current target detection methods based deep learning for transmission line,the feature extraction ability is poor for small target,easy to misdetection leakage detection,detection accu-racy is low,detection speed is slow.A transmission line target detection method was proposed based on an improved neural network model YOLOv7.Firstly,the MobileNetV2 network was used as the feature extraction part of YOLOv7 to achieve lightweight processing of the model.Secondly,the CA mecha-nism and ASPP module were introduced to improve the accuracy and perception of the model.Finally,the self-drawn transmission line obstacle data set was used for training.Improved YOLOv7 network an-dare compared with the original YOLOv7 model.The results show that the algorithm proposed has sig-nificantly improved the accuracy and recall rate,which meets the fault detection in complex scenarios and is more conducive to model deployment of mobile devices and embedded systems.
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