| Abstrakt: |
Nowadays, the target detection technology for synthetic aperture radar( SAR) images has been widely researched and applied, and the deep learning method can be effectively applied to ship detection under complex background features, and the detection accuracy and speed are also has certain improvement. In order to further improve the detection speed and accuracy in the SAR ship target scene, a fast detection method for a single type of target in SAR ship images is proposed. The YOLOv4-Tiny network is modified by applying depthwise separable convolution, and on top of this, improved channel and spatial attention mechanisms are added to improve accuracy. Taking the input image size of 608 x 608 as the benchmark, compared with the YOLOv4-Tiny model, the experimental results show that the detection accuracy of the method in this paper can reach 93.91%, the detection speed can reach 42.8 frame/s, and the model is improved by 11.7 frame/s. The weight is only 2.17 MB in size, which is about 1/10 of that of YOLOv4-Tiny. The model is conducive to the deployment of hardware on the board, and meets the needs of fast detection scenarios for ship targets in SAR images. [ABSTRACT FROM AUTHOR] |
