SAR Images Target Detection Based on YOLOv5
Wenkao Yang, Ziwei Zhang · 2021
Synthetic aperture radar (SAR) images target detection is widely used in military reconnaissance and civil surveillance because of its strong anti-jamming ability. However, the traditional SAR images target detection technology can not be applied to all detection conditions since the feature extraction relies too much on the imaging configuration. With the help of more thorough data analysis, deep learning is undoubtedly the best way to solve this problem, meanwhile it has better generalization capability and generality. In this paper, the author proposed to use latest YOLOv5 as the target detection algorithm network, which has good detection effects for small objects in large scenes; The author also carried out data enhancement, migration learning and other improved optimization methods, comparing with other excellent networks under the same conditions to verify the superiority and flexibility of the method in detection accuracy and speed. The final mean average precision (mAP) reaches 0.994.