Abstract: Target detection algorithms under big data often have the problem of missed target detection and repeated detection. To solve this problem, Mask RCNNAT-NMS algorithm based on AT-NMS is proposed. Firstly, a deformable convolution module is added on the basis of ResNet to enhance the ability to extract multi-layer convolution features of the target. Secondly, 
the AT-NMS algorithm is used to extract the in-depth information of the candidate target area in the RPN (Regional Candidate Network) stage. Thirdly, the positioning of the target is more accurate through two quantitative processing of ROI Align. Finally, three branches are used to achieve target instance segmentation, target classification and target border regression. The experimental results on the PASCAL-VOC2012 and Indoor CVPR_09 data sets show that, compared with the mask RCNN algorithm, the Mask RCNNAT-NMS algorithm reduces the repeated detection rate and the target missed detection rate, and improves the recognition accuracy. It can be seen that Mask RCNNAT-NMS algorithm can alleviate the problem of target missing and repeated detection caused by fixed threshold, and improve the detection accuracy on this basis.

Key words: target detection, Mask RCNN, adaptive threshold non-maximum supression, residual network