Research on Neural Network Post-Quantization Method for Ship Detection in SAR Images
Penghao Xiao, Xiaojing Lin, Haipeng Wang · IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium · 2022
Deep learning (DL) has been fast developed and widely used in Synthetic Aperture Radar (SAR) image target detection with the booming of AI technique. Many DL models have been proposed. However, most of them cannot work in embedded devices because of the limited hardware resource. In this paper, a post-quantification method for neural networks is proposed to reduce computational costs. Embedded devices usually support best for Float16 and Int8 low-bit data operations. A training procedure to preserve end-to-end model accuracy is designed, so that the bit depth of the model is compressed without significant loss of model accuracy, and can efficiently reduce the complexity of the model space, reducing the complexity of inference time. To verify our study, the quantified model is deployed on embedded devices to perform inference on 1024*1024 SAR images. The speed can reach 26FPS, which is 4 times faster than the inference speed before quantization.