Fig. 5: Visualization of underwater image generation and object detection results.
From: A diffusion model-based image generation framework for underwater object detection
A Shows the powerful generation capabilities of ULGF. Unlike mainstream underwater image generation methods (UWCNN, UWNR and UWGAN), ULGF does not require in-air images as input. ULGF is capable of generating underwater targets with specified layouts and types. Additionally, by providing different random seeds to ULGF, diverse images can be generated. In the demonstration, we randomly generate three sets of images for each layout label. B Illustrates the effect of fusing prior knowledge with noise for image generation, as well as the improvement in detection performance on the test dataset after augmenting the dataset with ULGF. After incorporating prior knowledge, the textures of the targets appear more natural. With the augmented dataset from ULGF, the detector shows a substantial reduction in both false negatives and false positives.
