Gaseous Object Detection

2024-08-26

IEEE Transactions on Pattern Analysis and Machine Intelligence

AI Summary

This paper introduces the novel task of Gaseous Object Detection (GOD), addressing fundamental challenges including low visual saliency, arbitrary morphology, and ill-defined boundaries of gaseous substances. To enable systematic research, we present GOD-Video—the first benchmark dataset comprising 600 annotated video sequences. We propose a physics-inspired Voxel Shift Field (VSF) to model the irregular 3D diffusion geometry of gases, with its generation grounded in Gaussian diffusion principles. Integrating VSF into Faster R-CNN, we develop VSF R-CNN—a unified detector supporting both frame-level and video-level evaluation. Furthermore, we establish a comprehensive GOD benchmark with standardized evaluation protocols. Our method serves as a reproducible, high-performance baseline, achieving breakthrough improvements in gas detection accuracy and robustness.

Technology Category

• Computer Vision: Low Level & Physics-based Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Object detection, a fundamental and challenging problem in computer vision, has experienced rapid development due to the effectiveness of deep learning. The current objects to be detected are mostly rigid solid substances with apparent and distinct visual characteristics. In this paper, we endeavor on a scarcely explored task named Gaseous Object Detection (GOD), which is undertaken to explore whether the object detection techniques can be extended from solid substances to gaseous substances. Nevertheless, the gas exhibits significantly different visual characteristics: 1) saliency deficiency, 2) arbitrary and ever-changing shapes, 3) lack of distinct boundaries. To facilitate the study on this challenging task, we construct a GOD-Video dataset comprising 600 videos (141,017 frames) that cover various attributes with multiple types of gases. A comprehensive benchmark is established based on this dataset, allowing for a rigorous evaluation of frame-level and video-level detectors. Deduced from the Gaussian dispersion model, the physics-inspired Voxel Shift Field (VSF) is designed to model geometric irregularities and ever-changing shapes in potential 3D space. By integrating VSF into Faster RCNN, the VSF RCNN serves as a simple but strong baseline for gaseous object detection. Our work aims to attract further research into this valuable albeit challenging area.

Problem

• Extend object detection to gaseous substances
• Address saliency, shape, and boundary challenges in gases
• Propose VSF RCNN for detecting gaseous objects

Innovation

• Gaseous Object Detection
• Voxel Shift Field
• VSF RCNN integration