Transcript
The paper proposes a novel data augmentation method for object detection that generates distorted versions of training images while maintaining a level of similarity to the original images. This method enhances the accuracy of models, such as YOLOv4, under various image distortions, achieving significant performance improvements when tested on the COCO and PASCAL datasets【1】【3】. Additionally, new adaptive attention mechanisms have been integrated into existing architectures, like YOLOv3, to further boost performance in detecting multi-scale objects【4】【5】.
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