A Diffusion-based Data Generator for Training Object Recognition Models in Ultra-Range Distance

• URL: http://arxiv.org/abs/2404.09846v2
• Date: Sat, 23 Nov 2024 16:55:30 GMT
• Title: A Diffusion-based Data Generator for Training Object Recognition Models in Ultra-Range Distance
• Authors: Eran Bamani, Eden Nissinman, Lisa Koenigsberg, Inbar Meir, Avishai Sintov,
• Abstract summary: Training a model to recognize hardly visible objects located in ultra-range requires an exhaustive collection of labeled samples. We propose the Diffusion in Ultra-Range (DUR) framework based on a Diffusion model to generate labeled images of distant objects in various scenes. DUR is compared to other types of generative models showcasing superiority both in fidelity and in recognition success rate when training a URGR model.
• Score: 2.240453048130742
• License:
• Abstract: Object recognition, commonly performed by a camera, is a fundamental requirement for robots to complete complex tasks. Some tasks require recognizing objects far from the robot's camera. A challenging example is Ultra-Range Gesture Recognition (URGR) in human-robot interaction where the user exhibits directive gestures at a distance of up to 25~m from the robot. However, training a model to recognize hardly visible objects located in ultra-range requires an exhaustive collection of a significant amount of labeled samples. The generation of synthetic training datasets is a recent solution to the lack of real-world data, while unable to properly replicate the realistic visual characteristics of distant objects in images. In this letter, we propose the Diffusion in Ultra-Range (DUR) framework based on a Diffusion model to generate labeled images of distant objects in various scenes. The DUR generator receives a desired distance and class (e.g., gesture) and outputs a corresponding synthetic image. We apply DUR to train a URGR model with directive gestures in which fine details of the gesturing hand are challenging to distinguish. DUR is compared to other types of generative models showcasing superiority both in fidelity and in recognition success rate when training a URGR model. More importantly, training a DUR model on a limited amount of real data and then using it to generate synthetic data for training a URGR model outperforms directly training the URGR model on real data. The synthetic-based URGR model is also demonstrated in gesture-based direction of a ground robot.

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