Stacked plate are counted by hand, which takes long time and has poor accuracy. Hence, the paper proposes a plate counting instrument based on embedded platform with a lightweight model. The instrument can detect in real time the number of stacked plate at production and logistics site, which deploys the improved Faster R-CNN network to the Industrial Personal Computer.In order to alleviate the difficulty of small object detection, the network algorithm by using lightweight network MobileNetv2 to integrate the efficient channel attention as the backbone network, using spatial attention and inverted residual structure module to reconstruct the FPN structure, proposing an HIOU_Loc algorithm based on on Height intersection over union to remove redundant prediction boxes. The plate counting experiment on a IPC equipped with N4100 CPU. The results show that the accuracy of the plate counting algorithm proposed in this paper reaches 98.51%, and it only takes 0.31 s to detect a high-resolution plate image. A quantitative calibration module is designed for the instrument. The instrument can reach 100% accuracy in counting stacked plate after the manual calibration module, which meets the requirements of stacked plate real-time counting in practical scenarios.