  Recent studies emphasize the crucial role of data augmentation in enhancing
the performance of object detection models. However,existing methodologies
often struggle to effectively harmonize dataset diversity with semantic
coordination.To bridge this gap, we introduce an innovative augmentation
technique leveraging pre-trained conditional diffusion models to mediate this
balance. Our approach encompasses the development of a Category Affinity
Matrix, meticulously designed to enhance dataset diversity, and a Surrounding
Region Alignment strategy, which ensures the preservation of semantic
coordination in the augmented images. Extensive experimental evaluations
confirm the efficacy of our method in enriching dataset diversity while
seamlessly maintaining semantic coordination. Our method yields substantial
average improvements of +1.4AP, +0.9AP, and +3.4AP over existing alternatives
on three distinct object detection models, respectively.
