RetinaNet is a typical representative of single-stage object detection, which can solve the problem of sample imbalance. However, due to the lack of region proposal extraction process in single-stage object detection, the effect of RetinaNet in dealing with the problem that object deviating from center or multi object crowding is not good. To solve this problem, we use a variety of optimization methods for RetinaNet to improve the accuracy of object detection. Firstly, FreeAnchor is introduced on the basis of RetinaNet, which can autonomously learn to match the target category; secondly, ResNeXt50 is taken as the backbone to improve the accuracy without increasing the parameter complexity; thirdly, Bottom-up Path Augmentation module is used to enhance the transmission of location information and further optimize the recall rate; finally, soft-NMS method is used to effectively reduce the false positive detection results and improve the average accuracy of object detection. We use the MS COCO data set to verify the new model. The mAP value of the new model reached 40.8, which is 4.3 more than baseline. It shows that the optimization methods are complementary to each other, which can effectively improve the object detection accuracy while ensuring the speed.

RetinaNet 是单阶段目标检测的典型代表，可以解决样本不平衡的问题。然而，由于在单阶段目标检测中缺少区域提议提取过程，RetinaNet在处理目标偏离中心或多目标拥挤问题时效果不佳。为了解决这个问题，我们对 RetinaNet 使用了多种优化方法来提高物体检测的准确性。首先在RetinaNet的基础上引入FreeAnchor，可以自主学习匹配目标类别；其次，以ResNeXt50为骨干，在不增加参数复杂度的情况下提高精度；第三，使用Bottom-up Path Augmentation模块，增强位置信息的传递，进一步优化召回率；最后，采用soft-NMS方法有效降低误报检测结果，提高目标检测的平均准确率。我们使用 MS COCO 数据集来验证新模型。新模型的 mAP 值达到了 40.8，比基线增加了 4.3。说明优化方法是互补的，在保证速度的同时，可以有效提高目标检测的准确率。