Boosting object detection via diffusion-based data augmentation

Object detection continues to be a significant challenge in computer vision. Despite advancements made possible through deep learning, these models predominantly depend on extensive and diverse annotated training data. Such data, unfortunately, often lacks representation of many real-world scenarios. To bridge this gap, we use target images from the original dataset to train a specialized generator. The main intent behind producing these images is to mimic the appearance of targets across a broader spectrum of real-world situations. Once integrated with the primary dataset, these synthetically generated images act as an effective augmentation to the original training set, encompassing scenarios and variations previously absent. This autonomous method eliminates the need for external data sources, proving to be more practical in most situations. Our empirical findings highlight significant improvements: with the ResNet-34 backbone, the mAP for SSD rose notably from 0.185 to 0.233. Furthermore, for small objects detected by Faster R-CNN with the ResNet-101 backbone, there is a pronounced improvement from 0.213 to 0.225. These results underscore our method's efficacy, especially in enhancing detection capabilities for underrepresented scenarios and smaller objects.