Home /Research /Research on Image Recognition of Power Inspection Robot Based on Improved YOLOv3 Model
OTHER

Research on Image Recognition of Power Inspection Robot Based on Improved YOLOv3 Model

Wei Xiong, Sha Yang, Zhao Zhang, Liang Chen, Shuxin Huang

Year
2020
Citations
3

Abstract

Abstract YOLO series models are widely used in power inspections, but they are prone to miss inspections for small targets and diverse targets. In response to this defect, an improved network model of YOLOv3-g suitable for GPU cores and an improved network model of YOLOv3 mini suitable for CPU cores are proposed. By reducing the number of small and medium-scale targets, the number of convolution kernels is increased, and the small and medium-scale targets are increased. The intensity of feature extraction reduces the amount of network calculations and better solves the problem of inaccurate detection and recognition of small targets in electric robot scenes.

Keywords

Computer scienceArtificial intelligenceConvolution (computer science)RobotFeature (linguistics)Image (mathematics)Scale (ratio)Feature extractionPower (physics)Pattern recognition (psychology)

Related papers

Browse all OTHER papers