Research And Design Of Smart Sand Plant Based On FPGA Hardware Acceleration

With the gradual depletion of natural sand,and the increasing awareness of environmental protection,mechanized sand is gradually replacing natural sand as the primary source of construction sand in China.However,the traditional sand plant management method suffers from low efficiency,wasted human resources,and manual intervention,which can no longer meet the needs of modern sand plant management.To respond to the national policy of digital transformation of the manufacturing industry and to make the management of sand plants more informative and intelligent,this thesis proposes a smart sand plant system based on FPGA hardware acceleration.The system is capable of license plate recognition,sand and gravel classification,automatic weighing,automatic deduction,safety helmet detection,and other sand plant management tasks.The system also provides a visual interface.The main results are as follows:(1)In the sand and gravel classification,constructs a multi-category sand and gravel dataset,uses data augmentation technology to expand the dataset to solve the problem of insufficient samples,and conducts research on sand and gravel classification based on convolutional neural networks.In safety helmet detection,a lightweight YOLOv4 network is proposed to ensure detection accuracy while compressing the network model and reducing hardware resource consumption.The algorithm is compared with other target detection models for experiments to verify the effectiveness and good performance of the algorithm through several metrics.(2)To reduce the server workload and power consumption,design and implements a FPGA-based deep learning hardware acceleration platform for deploying sand and gravel classification and safety helmet detection networks.The experimental results show that the Mobile Net V2 network performs best in the sand and gravel classification task,and the improved YOLOv4 in this thesis performs best in the safety helmet detection task.Therefore,Mobile Net V2 and the improved YOLOv4 network model are deployed into FPGA to implement the sand and gravel classification and safety helmet detection modules.The classification accuracy of sand and gravel is 98.65%,the detection m AP of the safety helmet is 95.29%,and the power consumption is only 11.6W.(3)Build a vehicle information collection device.Use the designed license plate recognition module and automatic weighing module to realize the automatic collection of vehicle license plate and weight information.(4)Build a smart sand plant platform to achieve intelligent management of the sand plant.Firstly,analyze the database requirements,design an E-R model,and complete the database construction based on the E-R model.Next,use the XOJO development tool to build a smart sand plant platform.The data collected by the sand plant collection device is processed,stored,managed,and visually displayed through the smart sand plant platform to improve the efficiency and management of the sand plant.