A new object detection method for object deviating from center or multi object crowding

Abstract 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.