Research and implementation of a fabric printing detection system based on a field programmable gate array and deep neural network

In this paper, aiming at the problems of difficult positioning, slow speed and low precision of digital printing, a detection system suitable for textile printing positioning is proposed and designed. This detection system innovatively combines a neural network and field programmable gate array (FPGA) to realize rapid and accurate positioning of printing. In the neural network part, this paper selects the backbone network Darknet19 of YOLOv2 as the backbone network, and under the premise of ensuring a certain detection accuracy, the network model is pruned and quantified to make it suitable for deployment on the embedded device FPGA. In addition, before the network training, this paper optimizes the candidate boxes by introducing k-means clustering to customize the analysis of the fabric print dataset to improve the detection accuracy. In the FPGA part, this paper optimizes the architecture on the FPGA side in two parts: data computation and data transmission. In terms of computational optimization, parallel optimization of the neural network is performed by combining FPGA optimization methods, such as pipeline and unroll. In terms of transmission optimization, we use a double-buffered design to ping-pong in the input and output modules to overlap the latency, and then use multi-port transmission to improve the overall bandwidth utilization and reduce the transmission latency caused by on-chip and off-chip interactions. The experimental results show that the detection system combining the neural network and FPGA can effectively position fabric prints and meet the needs of real-time. The design scheme has lower power compared to the graphics processing unit and is faster compared to the central processing unit.