Improved U-Net based insulator image segmentation method based on attention mechanism
Gujing Han, Min Zhang, Wenzhao Wu, Min He, Kaipei Liu, Liang Qin, Xia Liu
Abstract
To realize the accurate identification and segmentation of the insulator string in the complex background image with diverse appearance and obscuration, this paper proposes an insulator segmentation method based on improved U-Net. The algorithm embeds the attention mechanism ECA-Net (Efficient Channel Attention Neural Networks) in the coding stage of U-Net to improve the model’s ability to extract semantic features, thereby improving the accuracy of insulator detection. Experimental results show that the average overlap IOU of the proposed method is 96.8%, which can more accurately segment different types of insulators in complex backgrounds.
Topics & Concepts
Image segmentationSegmentationArtificial intelligenceMechanism (biology)Computer scienceComputer visionNet (polyhedron)Image (mathematics)Insulator (electricity)Pattern recognition (psychology)Materials scienceMathematicsOptoelectronicsPhysicsQuantum mechanicsGeometryImage Enhancement TechniquesPower Line Inspection RobotsAdvanced Neural Network Applications