Laser & Optoelectronics Progress, Volume. 58, Issue 12, 1210013(2021)
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.
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Junkai Luo, Baohua Zhang, Yanyue Zhang, Yu Gu, Yueming Wang, Xin Liu, Yan Ren, Jianjun Li, Ming Zhang. An Object Detection Algorithm Based on Contextual Self-Calibration And Dual-Attention Mechanism[J]. Laser & Optoelectronics Progress, 2021, 58(12): 1210013
Category: Image Processing
Received: Sep. 9, 2020
Accepted: Sep. 30, 2020
Published Online: Jun. 18, 2021
The Author Email: Zhang Baohua (zbh_wj2004@imust.cn)