Abstract: Aiming at the problem that D2Det(Towards High Quality Object Detection and Instance Segmentation) has poor detection effect and large parameter quantity in the face of scale change targets and small targets,this paper proposes a scale adaptive target detection model G-SAD2Det based on D2Det.Firstly,in the data preprocessing stage,the data enhancement algorithms CutOut and Mosaic are introduced,and the model has good robustness when dealing with complex scenes.Secondly,the feature extraction network ResNet is improved,the multi-scale feature extraction structure is built in each residual block,and the target features are better extracted from the fine-grained level.At the same time,the switchable global context semantic feature extraction module is added to the network structure,and the salience features and global context semantic information are enhanced through different pooling layers.Then,the candidate frame generation module is improved,and the center area of the self-locating target is used to guide the generation of the candidate frame,so that the adaptive ability of the algorithm to the scaling target can be enhanced.Finally,replacing ordinary convolution with Ghost convolution to reduce the amount of network parameters and computation.VOC data set and COCO sub-data set are used to verify the effectiveness of the algorithm,the mAP@0.5 value of G-SAD2Det increases by 3.6% and 4.9% respectively,compared with D2Det in the two data sets.The number of model parameters reduces by 27.42% and the amount of calculation reduces by 35.96%.It is proved that the improved algorithm not only improves the accuracy,but also reduces the amount of computation.

Key words: Object detection, Scale adaptive, Multi-scale feature extraction, Residual element, Regional guidance candidate box