Resumen
In order to monitor traffic in congested waters, permanent video stations are now commonly used on interior riverbank bases. It is frequently challenging to identify ships properly and effectively in such images because of the intricate backdrop scenery and overlap between ships brought on by the fixed camera location. This work proposes Ship R-CNN(SR-CNN), a Faster R-CNN-based ship target identification algorithm with improved feature fusion and non-maximum suppression (NMS). The SR-CNN approach can produce more accurate target prediction frames for prediction frames with distance intersection over union (DIOU) larger than a specific threshold in the same class weighted by confidence scores, which can enhance the model?s detection ability in ship-dense conditions. The SR-CNN approach in NMS replaces the intersection over union (IOU) filtering criterion, which solely takes into account the overlap of prediction frames, while DIOU, also takes into account the centroid distance. The screening procedure in NMS, which is based on a greedy method, is then improved by the SR-CNN technique by including a confidence decay function. In order to generate more precise target prediction frames and enhance the model?s detection performance in ship-dense scenarios, the proposed SR-CNN technique weights prediction frames in the same class with DIOU greater than a predetermined threshold by the confidence score. Additionally, the SR-CNN methodology uses two feature weighting methods based on the channel domain attention mechanism and regularized weights to provide a more appropriate feature fusion for the issue of a difficult ship from background differentiation in busy waters. By gathering images of ship monitoring, a ship dataset is created to conduct comparative testing. The experimental results demonstrate that, when compared to the three traditional two-stage target detection algorithms Faster R-CNN, Cascade R-CNN, and Libra R-CNN, this paper?s algorithm Ship R-CNN can effectively identify ship targets in the complex background of far-shore scenes where the distinction between the complex background and the ship targets is low. The suggested approach can enhance detection and decrease misses for small ship targets where it is challenging to distinguish between ship targets and complex background objects in a far-shore setting.