Boat object detection method based on improved Cascade R-CNN algorithm
To address the issue of low accuracy in boat object detection in real-world scenarios,this paper improves upon the Cascade R-CNN algorithm and proposes a boat object detection method called Boat R-CNN.Boat R-CNN utilizes the Swin-Transformer Tiny network with a self-attention mechanism to extract image features,employs Soft-NMS for non-maximum suppression to enhance the filtering precision of candidate bounding boxes,uses the Smooth_L 1 loss function to accelerate model convergence and reduce gradient explosion,and utilizes CIOU bounding box regression loss to improve the quality of candidate box regression.Furthermore,the aspect ratio of anchor boxes is optimized for the shape characteristics of boat objects,improving the quality of anchor box generation.Experimental results have shown that the Boat R-CNN al-gorithm has increased accuracy by 21.8％compared to the original Cascade R-CNN algorithm and 30.3％compared to the mainstream Faster R-CNN algorithm.Boat R-CNN effectively improves the accuracy of boat object detection in real-world scenarios.
万方数据
维普
