![]() ![]() Compared to typical object detection networks, the proposed network achieves an AP value of 0.877, which is 7.4% points higher than the original network. By employing the SIoU loss calculation method, the proposed RS-RCNN network enhances the representation of global context information and local semantic information, while improving fusion efficiency and detection accuracy. Moreover, it integrates the AAM_HRFPN (Attention Aggregation Module High resolution network) multi-feature fusion network and incorporates a linear attention mechanism. This algorithm incorporates the fusion of ResNet_50 and Swin Transformer networks as backbone networks for feature extraction. To overcome this issue, an RS-RCNN (ResNet_50 + Swin Transformer RCNN) object detection algorithm is proposed as an extension of Faster-RCNN. The limitations of deep learning detection algorithms based on convolutional neural networks stem from the local characteristics inherent in convolutional operations.
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