An Object Detection Algorithm Based on Contextual Self-Calibration And Dual-Attention Mechanism

To prevent numerous hyperparameters and to overcome poor generalization ability and imbalance between positive and negative samples in anchor-based multiclass object detection algorithms, an object detection algorithm based on an improved anchor-free method is proposed herein. To address the difficulty faced by traditional algorithms in obtaining robust feature representations in multiclass object detection tasks, a self-calibration dual attention module based on contextual combination is first constructed herein. It obtains the multireceptive field information through a mixed dilated convolution group. Then, a low-dimensional spatial embedding method is self-calibrated to obtain the contextual spatial information. Finally, the spatial information and channel information are combined to enhance the feature representation ability of the proposed algorithm. To prevent the usual introduction of background noise owing to large changes of object scale and irregular appearance in multiclass object detection tasks, the improved deformable convolution is used to adaptively sample the target position. Experimental results obtained using the large multiclass object detection data set MSCOCO show that the proposed algorithm can effectively improve the detection accuracy of multiclass object and outperforms the existing detection algorithms.