In response to the problem of insufficient feature extraction and insufficient detection accuracy in the anchor-free frame object detection algorithm CenterNet.We propose an improved algorithm based on dual-angle multi-scale feature fusion.Firstly,using a repeated weighted bidirectional feature pyramid network to enhance the fusion ability of multi-scale weighted features from a hierarchical perspective.Secondly,by replacing the backbone network with Res2Net network,the network can improve its multi-scale expression ability from a more fine-grained perspective.Finally,the coordinate attention mechanism is added to enhance the receptive field without consuming a lot of computing resources,and the coordinate information is embedded in the channel attention to improve the model's target positioning and improve the detection accuracy of the model.The improved algorithm's detection accuracy in the PASCAL VOC data set and KITTI data set reached 82.3%and 87.8%respectively.Compared with the original CenterNet algorithm,the accuracy increased by 5.5%and 2.4%respectively.
维普
万方数据