Lightweight Detection Method for Pointer Meters Based on SCC-YOLO
To address the issue of difficult deployment caused by the complex structure,high memory usage,and large param-eter calculation of pointer instrument detection,a lightweight instrumentation target detection network SCC-YOLO based on YOLOv5 was proposed.The network backbone was redesigned by using the lightweight backbone ShuffleBlock_lite structure,and the depth separable convolution reconstructed by convolution kernel was introduced to further improve the feature extraction capa-bility through the SimAM parameter-free attention mechanism module to further enhance the feature extraction capability.Fusing coordinate convolution CoordConv with CARAFE lightweight upsampling module improved the model feature fusion performance.Data enhancement techniques were utilized to construct pointer gauge image datasets in real scenes and in complex scenes.Com-parative experimental results show that the SCC-YOLO model can significantly improve the detection efficiency of pointer gauges,with an average reduction of 27.3%in the number of parameters of the model,an average reduction of 54.8%in the computa-tion,and an integrated improvement of 1.3%in the accuracy.The lightweight design makes it easier to be deployed on mobile and edge devices,and can meet the requirements of pointer meter detection tasks in real scenarios.
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