The problem of poor segmentation of diabetic retinal vascular images has always existed. In this paper, we propose an improved U-Net architecture RAFF-UNet to address it. In the U-Net encoding module, we innovatively use RAFF attention module before down-sampling. Based on the retinal vascular segmentation dataset made by Baidu PaddlePaddle AI STUDIO, the experimental results show that compared with U-Net and Attention-UNet, the MIoU index of RAFFUNet is increased by 0.94% and 1.33% respectively, and Acc index is improved.
Object detection is an important task in computer vision. There are many practical applications using object detection based on deep learning nowadays. For deployment on FPGA with limited resource and operator support, object detection faces problems such as how to improve speed and reduce power consumption. YOLOX is a high-performance anchor-free YOLO version. To deploy YOLOX network on FPGA, we first replace the Focus layer of YOLOX, adjust the structure of the SPP layer, and change the activation function to meet the operator support constraints. Then we perform sparse training and use scaling factors of BN layer to select out the insignificant channels. The convolutional layer channels are pruned according to the degree of sparseness and pruning ratios. Finally, the network is quantified, compiled via the Vitis AI tool, and deployed on the Xilinx FPGA development board. Comparing the performance with different pruning ratios, the experiments demonstrate that the network runs significantly faster on the FPGA after pruning, and the power consumption is also reduced.
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