基于CenterNet目标检测算法的改进模型

在原CenterNet算法中，以Hourglass为Backbone的目标检测模型平均精度均值高于one-stage算法，但检测速度较低。为此，基于原有CenterNet目标检测算法，对Hourglass-104模型进行改进，设计一种Hourglass-208模型，并给出双特征金字塔网络特征图融合方法。在此基础上对目标大小和训练采用smooth L₁损失函数，提出一种新的可端到端训练的目标检测算法T_CenterNet。在MS COCO数据集上的实验结果表明，该算法目标检测的评估指标AP₅₀、AP_S、AP_M分别为63.6%、31.6%、45.8%，检测速度达到36 frame/s，综合性能优于原CenterNet算法。

Improved Model Based on CenterNet Object Detection Algorithms

In the original CenterNet algorithms, the object detection model with Hourglass as Backbone has a higher mean Average Precision(mAP) than other one-stage algorithms, but it is limited by the low detection speed.To address the problem, a new model named Hourglass-208 is proposed by using the original CenterNet object detection algorithm to improve the Hourglass-104 model.Additionally, a feature map fusion method for Twin Feature Pyramid Networks(TFPN) is given.On this basis, smooth L1 is used for the loss function of the object size to establish a new object detection algorithm, T_CenterNet, which can perform end-to-end training.Experimental results on the MS COCO data set show that the target detection evaluation index AP₅₀, AP_S, AP_M of the proposed algorithm are 63.6%, 31.6%, 45.8%, respectively, and the detection speed of the algorithm reaches 36 frame/s.The comprehensive performance of the proposed algorithm is better than that of the original CenterNet algorithm.