Jian, J.; Liu, L.; Zhang, Y.; Xu, K.; Yang, J. Optical Remote Sensing Ship Recognition and Classification Based on Improved YOLOv5. Remote Sens.2023, 15, 4319.
Jian, J.; Liu, L.; Zhang, Y.; Xu, K.; Yang, J. Optical Remote Sensing Ship Recognition and Classification Based on Improved YOLOv5. Remote Sens. 2023, 15, 4319.
Jian, J.; Liu, L.; Zhang, Y.; Xu, K.; Yang, J. Optical Remote Sensing Ship Recognition and Classification Based on Improved YOLOv5. Remote Sens.2023, 15, 4319.
Jian, J.; Liu, L.; Zhang, Y.; Xu, K.; Yang, J. Optical Remote Sensing Ship Recognition and Classification Based on Improved YOLOv5. Remote Sens. 2023, 15, 4319.
Abstract
Due to the special characteristics of the shooting distance and angle of remote sensing satellites, the pixel area ratio of ship targets is small and the feature expression is insufficient, which leads to unsatisfactory ship detection performance and even situations such as missed detection and false detection. In this study, we propose an improved-YOLOv5 algorithm. The improvement strategies mainly include: (1) Add the Convolutional Block Attention Module (CBAM) into the Backbone to enhance the extraction of target-adaptive optimal features; (2) Introduce cross-layer connection channel and lightweight GSConv structure into the Neck to achieve higher-level multi-scale feature fusion and reduce the number of model parameters; (3) The Wise-IoU loss function is used to cal-culate the localization loss in the Output, and assign reasonable gradient gains to cope with dif-ferences in image quality. In addition, during the preprocessing stage of experimental data, a me-dian and bilateral filter method is used for noise reduction to reduce interference from ripples and waves and highlight the information of ship features. The experimental results show that Im-proved-YOLOv5 has a significant improvement in recognition accuracy compared to various mainstream target detection algorithms; Compared to the original YOLOv5s, the mean Average Precision (mAP) has improved by 3.2% and the Frames Per Second (FPN) has accelerated by 8.7%.
Copyright:
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