PR-DETR: Extracting and utilizing prior knowledge for improved end-to-end object detection

Abstract

The query initialization in the Transformer-based target detection algorithm has static characteristics, resulting in a limitation to flexibly adjust the degree of attention to different image features during the learning process. In addition, without the guidance of global spatial semantic information, it will cause the model to disregard the relationship between the target and the surrounding environment due to relying on local features for target detection, causing the problem of false detection or missed detection of the target. In order to solve the above problems, this paper proposes a query-optimized target detection model PR-DETR based on feature map guidance. PR-DETR designs the Aggregating Global Spatial Semantic Information module (AGSSI module) to extract and enhance global spatial semantic information. Afterwards, we design queries that participate in the interaction of local and global spatial semantic information in the encoding part in advance, so as to obtain sufficient prior knowledge and provide more accurate and efficient queries for subsequent decoding feature maps. Experiment results show that PR-DETR has significantly improved detection accuracy on the MS COCO data set compared with existing related research work. The mAP is 3.5, 2.3 and 2.0 higher than Conditional-DETR, Anchor-DETR and DAB-DETR respectively.