Abstract: Object detection aims to achieve accurate recognition and localization of objects in images and is an important research area in computer vision.Deep learning-based object detection has made great progress,but there are still shortcomings.The semantic information brought by large down-sampling coefficients is beneficial to image classification,but the down-sampling process inevitably brings information loss,resulting in insufficient model feature extraction and thus a decrease in detection accuracy.To address these problems,this paper proposes a receptive field enhancement and multi-branch aggregation network for object detection.First,the receptive field enhancement module is designed to expand the receptive field of the backbone network.This module can acquire object context cues and can alleviate the problem of object information loss during down-sampling because it does not change the feature spatial resolution.Then,in order to take full advantage of the localization of convolutional neural networks and the long-range feature-dependent property of the self-attention mechanism,the receptive field expanding composite backbone network is constructed to retain local features as well as to improve the global feature perception capability of the model.Finally,a multi-branch aggregation detection head network is proposed to form information flow between three prediction branches and fuse feature information between branches to improve the detection capability of the model.Validation experiments are carried out on MS COCO datasets,and the results show that the average accuracy of the proposed model is better than that of many mainstream object detection models.

Key words: Object detection, Self-attention mechanism, Receptive field expansion, Feature fusion, Decoupled head