Significant advancements in ship detection technology in synthetic aperture radar (SAR) images have been made in recent years due to the rapid development of deep learning technology. Nevertheless, there are still certain issues with the current deep learning-based detection algorithms when detecting small-scale, arbitrary-direction, and densely packed ship targets in SAR images. We offer a rotating target detection network based on improved YOLOv8 as a solution to these issues. First, a new small target sampling layer is introduced in YOLOv8 to enhance the model’s ability to detect small-target objects by reducing the down-sampling rate. Second, the learnable weights are introduced to adaptively adjust the feature information at different scales and superimpose shallow features on deep features to better extract feature information when the model conducts feature fusion. Finally, the offset of the predicted bounding box concerning the true bounding box can be expressed more accurately using distribution focal loss and probabilistic intersection-over-union as the loss functions. Extensive experiments are conducted on rotated ship detection dataset in SAR images (RSDD-SAR) and SAR ship detection dataset (SSDD) to demonstrate the effectiveness of the improvement. BP-YOLOv8 achieved an F1 score of 93.01% and 97.48% on the RSDD-SAR and SSDD datasets, with an average precision of 95.87% and 98.82%, respectively.
Countersink quality detection using point cloud is an effective way to detect defect of countersink with high precision of point cloud. This paper proposes an algorithm for segmenting surface of countersink using point cloud. For features of countersink and requirement for Countersink quality detection, this algorithm based on normal vector estimation and smoothing operator. Experimental results demonstrate that the proposed algorithm can be considered as an accuracy, precision, and real-time approach for point cloud segmentation with inevitable disturbances in terms of the segmentation quality and CPU time.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.