Target Detection Label Assignment Method Based on Global Information - ZHANG Pei-pei; LU Zhen-yu (Journal of Harbin University of Science and Technology)
With the development of deep learning framework, new object detection algorithms have also been proposed, such as first-stage and two-stage detection models, which have improved the detection speed and solved the problem of object detection at different scales, but they have not yet been well solved for overlapping, occlusion and other issues. One of the reasons for this problem is that during model training, label assignment is not done well. Aiming at this problem, this paper proposes a target detection label allocation method based on global information, which uses the assignment method to establish a global optimal label allocation mathematical model based on the loss function in the model training stage, and gives the fusion mode of the model with other object detection models, and the role played by the method in the process of object detection. The experimental results show that the detection model of the fusion method is better than that of the model that does not use the method under the complex detection scenarios such as overlapping and occlusion.