Abstract: The construction of a maritime power is a current strategic direction for China’s vigorous development.In response to the low detection and classification recognition rate and slow operation speed of existing deep learning-based remote sensing image ship target detection and classification algorithms on embedded platforms,this paper proposes an improved Mix-YOLO network model based on the Cambricon-MLU220 embedded platform.The model is based on the YOLOv7-tiny network as the basic framework.Firstly,the MobileNet series network module is introduced to replace the feature extraction network partially,reducing the network parameter volume.Then,the ULSAM attention mechanism is introduced to enhance the network’s learning and classification ability,reducing the false alarm rate.Finally,in order to make the detection speed improvement effect more obvious on the embedded platform,the network model is programmed by splitting the large module into small modules.Experimental results show that the Mix-YOLO algorithm reduces the parameter volume and calculation by 39.70% and 29.70%,respectively,on the basis of the original YOLOv7-tiny network.The processing frame rate is increased from 97.27 fps to 120.88 fps,and the accuracy is improved by 7.7%.It can achieve real-time detection and recognition of ship targets in remote sensing images.

Key words: Optical remote sensing image, Ship detection, Lightweight network, Attention mechanism, Embedded platform