RS-RCNN: an indoor window detection algorithm for autonomous spraying robot

Abstract

Abstract The limitations of deep learning detection algorithms based on convolutional neural networks stem from the local characteristics inherent in convolutional operations. To overcome this issue, an RS-RCNN (ResNet_50 + Swin Transformer RCNN) object detection algorithm is proposed as an extension of Faster-RCNN. This algorithm incorporates the fusion of ResNet_50 and Swin Transformer networks as backbone networks for feature extraction. Moreover, it integrates the AAM_HRFPN (Attention Aggregation Module High resolution network) multi-feature fusion network and incorporates a linear attention mechanism. 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. 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. The successful application of this method presents a novel solution for robot detection of non-spraying areas.