Abstract: A novel algorithm named as target detection algorithm based on compound scaling deep iterative CNN by regression converging and scaling mixture is proposed to avoid the disadvantages of low robustness,label marginalization and poor convergence performance of the regression loss function in the EfficientDet algorithm.After utilizing the 2×2 scaling mixture regularization strategy to enhance the training samples,the proposed method avoids the over fitting and improves the generalization ability of the model.The convergence speed,the positioning accuracy and the CNN regression accuracy are improved,since the aspect ratio and the center distance are taken into account in the penalty items of the CIOU loss function that can predict the bounding frame coordinate and suppress the redundant boxes.The proposed method improves the label fault tolerance rate because the cross entropy loss with label smoothing for class is established after generating the label smoothing regularization distribution,which is a weighted sum of the marginal label distribution and the uniform distribution by setting the smoothing parameter.Experiments are performed on the PASCAL VOC 2007 and 2012 datasets,and the results show that while the number of the network model parameters remain unchanged,the mean average precision of the proposed algorithm reaches 88.31 %,which is 3.29% higher than that of the original network(EfficientDet-D2,84.12%).Compared with YOLOv4,YOLOv3,SSD,Faster R-CNN and Fast R-CNN,the mean average precision increases by 5.2%,10.71 %,14.01%,15.11% and 18.30 %,respectively,and the number of network model parameters is reduced by 55.94×10⁶,52.91×10⁶,16.09×10⁶,55.18×10⁶ and 53.11×10⁶,respectively.Not only the algorithm improves the detection accuracy and the F1 score,but also it takes 0.73 s to detect each test image,which meets the real-time requirements during the detecting phase.

Key words: Object detection, EfficientDet, IOU, Label smoothing