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The detection and positioning of the filter bag opening occupies a crucial position in the intelligent production process of the filter bag. However, due to the flexible of the filter bag, it is difficult to carry out conventional detection methods effectively, and the positioning accuracy cannot meet the production requirements. In this paper, a multi-scale target detector combining deformable convolution and mask information is proposed. This detector uses deformable convolution to improve fixed convolution in the high layer of the backbone network, combined with feature pyramid technology to achieve multi-scale information fusion. The obtained multi-scale information is then used to generate candidate regions through the region proposal network, which is screened using the improved Soft-NMS method, and finally sent to the detection head for recognition and segmentation. In this paper, experiments were carried out on the filter bag image data set. The results show that the proposed algorithm achieves accurate identification and high-precision positioning of the filter bag opening.