Safety Fence Image Segmentation Technology Based on Hyperparameter-optimized Lightweight Deep Neural Networks
Standardizing the installation of safety fences during power poles and towers operation is an important means to ensure safety.Currently,Artificial Intelligence technology is widely used in the detection of safety fence installation in power poles and towers operation.Using image segmentation to extract the construction region from the background image can effectively reduce image complexity and improve accuracy of Artificial Intelligence detection technology.The image segmentation based on Deep Neural Networks is an effective way to achieve this goal,but the Deep Neural Networks has complex structures and large parameters,which needs lots of computing resources and time.Therefore,this paper proposes a lightweight Deep Neural Networks for segmentation of safety fence images,and uses Hyperparameter-optimized means to automatically adjust the hyperparameter settings,so as to improve network model performance and achieve better segmentation results.Experimental results show that the proposed method can significantly reduce the amount of network model parameters while achieving a segmentation accuracy higher than 90%.
electric power safety fenceimage segmentationDeep Learninglightweight
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