In order to improve the problem of lack of spatial detail information in feature maps, failure of target features to interact with global context instance, and insufficient learning of global semantic information, a sparse set object detection algorithm combining adaptive feature augmentation and instance feature interaction is designed. In the process of feature extraction, the adaptive feature augmentation module uses pooling and convolution at different scales to enrich high-level semantic information, and reduces noise interference such as the low-level semantic information background. Meanwhile, it decreases the rate of false detection and missed detection. In design of bounding box regression, the instance feature interaction module combines multi-layer attention of transformer which enhances the channel information of the proposal box. Channel attention and dynamic convolution network are also employed to improve the edge information of the object and increase the interaction efficiency of the network instance feature. Finally, experiment results show that the average accuracy of COCO2017 dataset is improved by 4.2%, 4.6% on the large target, and 2.7% on PASCAL VOC dataset, respectively.
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