【摘要】 为了提升无锚框目标检测模型对物体多尺度检测性能，并实现检测速度与精度的最佳折中，提出一种具有抗锯齿能力的无锚框目标检测模型。下采样操作中，使用分组自适应低通滤波器解决网络中存在的锯齿问题；并联不同空洞率的空洞卷积进行多尺度特征融合，扩大神经元感受野范围。防止在模型训练过程中破坏网络参数，对损失函数进行实验讨论，替换为smooth L1 Loss函数。实验结果表明，在PASCAL VOC数据集上mAP指标达到了82.1%,FPS达到了32,与CenterNet-ResNet101相比，mAP提升了4.3%,FPS提升了18.5%。更多还原

【Abstract】 In order to improve the multi-scale detection ability of anchor-free object detectors and achieve the best compromise between detection speed and accuracy, a new anchor-free object detector with anti-aliasing ability is proposed. In the process of down-sampling, the grouped low-pass filters were used to prevent aliasing in the network, so as to improve the robustness of the model. The atrous convolutions with different atrous rates were used to fuse the multi-scale information of the objects and expand the receptive field of the neurons. The network parameters were prevented from being destroyed in the process of model training, and a better training model was obtained by using smooth L1 Loss function. The experimental results show that on Pascal VOC datasets, the mAP reaches 82.1%, and FPS reaches 32. Compared with the CenterNet model, the accuracy and speed of the model are improved. The mAP and FPS are improved by 4.3% and 18.5% compared with the original CenterNet-ResNet101 model.更多还原