Abstract:

At present,most object detection algorithms have poor performance because of the large span of scales,leading to errors and omissions.To address the above issues,a multi-scale object detection algorithm combined with the super-resolution technology is proposed in this paper.First,based on the one-stage YOLO framework,the super-resolution module is employed to the neck network during the process of multi-scale feature fusion,which avoids further loss of detailed features in deeper layers.Second,the attention module is integrated in the shallower layers to focus on the channel information on object contour features and to suppress irrelevant features,thus improving the superficial representational capacity.Finally,ablation and comparative experiments are carried out on PASCAL VOC 2007 and MS COCO 2017 public datasets.Experimental results show that the proposed module can improve the detection performance.Compared with the current contrast algorithms,not only can the average accuracy rate of small,medium and large objects be increased by 1.20%,1.20% and 1.30%,but also the average recall rate can be improved by 4.20%,3.50% and 4.20%,respectively.

Key words: multi-scale object detection, super-resolution technology, attention mechanism, deep learning