Gaseous Object Detection

TL;DR

This paper introduces the novel task of gaseous object detection, develops a dedicated dataset and benchmark, and proposes a physics-inspired model to effectively detect gases with irregular shapes and lacking clear boundaries.

Contribution

It extends object detection techniques from solids to gases, creating a new dataset, benchmark, and a physics-inspired detection model for gaseous substances.

Findings

The dataset contains 600 videos with over 141,000 frames.

The VSF RCNN baseline demonstrates effective gaseous object detection.

Benchmark results establish a foundation for future research in this area.

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.