Object Detection Algorithm Based on Improved Split-attention Network

Abstract

Recently,most object detection algorithms based on convolutional neural network have the problems of lacking of reasonable use of meaningful contextual information and are easy to miss the detection of hard targets.In order to solve these problems,this paper proposes an object detection algorithm based on improved split-attention networks.Firstly,the split attention mechanism is introduced,and the multi-path structure is combined with feature-map attention mechanism to improve its feature representations.Then,in the convolution layer,poly-scale convolution is used to replace the vanilla convolution to enhance the scale-sensitivity of the neural network.Finally,the proposed algorithm is applied to Faster R-CNN.Experiments are carried out on Pascal VOC and MS COCO datasets.Compared with the original algorithm,the mAP of the proposed algorithm has improved 1.6% and 2.4% respectively without introducing additional parameters and computational complexities,and the mAP of the proposed algorithm is also higher than that of other algorithms,which verifies its good performance.

Keywords

• convolutional neural network|contextual information|object detection|split-attention|poly-scale convolution