weathon/sim_gas

This repository contains the data presented in LangGas: Introducing Language in Selective Zero-Shot Background Subtraction for Semi-Transparent Gas Leak Detection with a New Dataset.

LangGas: Introducing Language in Selective Zero-Shot Background Subtraction for Semi-Transparent Gas Leak Detection with a New Dataset

Wenqi Guo, Yiyang Du, Shan Du University of British Columbia Group of Methane Emission Observation and Watch (MEOW), Weasoft Software

Latina 繁体中文 Mewspeak

Abstract

Gas leakage poses a significant hazard that requires prevention. Traditionally, human inspection has been used for detection, a slow and labour-intensive process. Recent research has applied machine learning techniques to this problem, yet there remains a shortage of high-quality, publicly available datasets. This paper introduces a synthetic dataset, SimGas, featuring diverse backgrounds, interfering foreground objects, diverse leak locations, and precise segmentation ground truth. We propose a zero-shot method that combines background subtraction, zero-shot object detection, filtering, and segmentation to leverage this dataset. Experimental results indicate that our approach significantly outperforms baseline methods based solely on background subtraction and zero-shot object detection with segmentation, reaching an IoU of 69% overall. We also present an analysis of various prompt configurations and threshold settings to provide deeper insights into the performance of our method.

Method Overview

Dataset Overview

You can preview more here.

Pre-computed Masks

result_full has all pre-computed masks and animated webp images for SimGas, and gasvid_res_full has all pre-computed masks and webp for GasVid. You can preview these animated webp: SimGas GasVid.

Results

BGS	VLM Filtering	Temporal Filtering	Seg.	τ₍VLM₎	Stationary Foreground I/P/R/FLA	Moving Foreground I/P/R/FLA	Overall I/P/R/FLA
✓			None	-	0.56 / 0.64 / 0.83 / 0.85	0.38 / 0.53 / 0.58 / 0.69	0.5 / 0.61 / 0.73 / 0.79
✓	✓		SAM 2	0.09	0.67 / 0.81 / 0.79 / 0.88	0.54 / 0.79 / 0.65 / 0.83	0.62 / 0.80 / 0.74 / 0.86
	✓	✓	SAM 2	0.19	0.22 / 0.39 / 0.28 / 0.57	0.46 / 0.65 / 0.59 / 0.74	0.31 / 0.49 / 0.4 / 0.63
✓	✓	✓	Trad.	0.12	0.57 / 0.85 / 0.65 / 0.83	0.35 / 0.88 / 0.37 / 0.72	0.49 / 0.86 / 0.55 / 0.79
✓	✓	✓	SAM 2	0.12	0.70 / 0.83 / 0.82 / 0.87	0.69 / 0.79 / 0.84 / 0.92	0.69 / 0.82 / 0.82 / 0.89

Table: Ablation study of different components with IoU (I), Precision (P), Recall (R), and frame-level accuracy (FLA). In the segmentation column (Seg.), traditional (Trad.) means Otsu [Otsu] combined with morphological transformations. This analysis corresponds to our ablation study, detailed in Section 4 of the paper.

Warning: Different methods of calculating IoU can produce inconsistent results. We used per video aggregation and then average across all videos.

Test on SimGas

Clone the GitHub repo (not this repo) https://github.com/weathon/Lang-Gas/

Step 1 Install required packages

Install pytorch according to https://pytorch.org/get-started/locally/

pip3 install opencv-python transformers tqdm Pillow wandb matplotlib scipy

Then, run

pip3 install bitesandbytes 'accelerate>=0.26.0'

Step 2: Download and Install SAM-2

git clone https://github.com/facebookresearch/sam2.git
mv sam2 .sam2
cd .sam2
pip3 install -e .

If installation fails, run:

echo -e '[build-system]\nrequires = [\n    "setuptools>=62.3.0,<75.9",\n    "torch>=2.5.1",\n    ]\nbuild-backend = "setuptools.build_meta"' > pyproject.toml

(See https://github.com/facebookresearch/sam2/issues/611 for more) Then run:

pip3 install -e .

Download the checkpoints:

cd checkpoints
bash download_ckpts.sh

More details: https://github.com/facebookresearch/sam2

Step 3. Download Dataset

Google Drive

Go to https://forms.gle/dJqHdiEN5u8gbVT98 and download the dataset. Put the videos of the downloaded dataset into the simulated_gas folder, and run python3 dataprep.py. Remove video 24, 26, 27, 28 as needed. (see the paper)

HuggingFace

If you want to use this dataset with HuggingFace, you can download it from here. However, our demo is not integrated with HF, but you can use it in your own project. The HF dataset does not include video 24 to ensure same length for input and groundtruth.

We did not provide train-test split as our method is zero-shot. when spliting the dataset, we strongly to split based on video instead of frames.

Step 4. Run the code

Modify owl_notracking.py for SAM-2 path.

Change sam2_checkpoint = "../../.sam2/checkpoints/sam2.1_hiera_small.pt" to your SAM-2 checkpoint path. Do NOT change config path.

Run the code

Run python3 owl_notracking.py with the following options. If you used dataprep.py, the root path should be sim.

Usage: owl_notracking.py [OPTIONS]

Options:
  --video_id TEXT           Video ID, such as vid20
  --root_path TEXT          Root path to dataset (default: "../sim")
  --display TEXT            Display server for visualization (default: "localhost:10.0")
  --log_file TEXT           Output log file name (default: "results.csv")
  --temporal_filter         Enable temporal filtering (flag)
  --vlm_threashold FLOAT    Threshold for VLM decision (default: 0.12)
  --positive_prompt TEXT    Positive prompt for VLM analysis (default: "white steam")

To reproduce our results, use --temporal_filter flag

Test on GasVid

Preparation

1. Download the dataset from the original author:
   https://drive.google.com/drive/folders/1JKEMtCGPSq2IqGk4uXZP9A6gr8fyJsGC

2. Remove all videos captured at depths greater than 18 meters as specified in the original paper.

3. Place the remaining videos in the Videos directory.

4. Run convert_gasvid.sh to extract frames.

Running the Test

1. Edit run_gasvid.py and owl_gasvid.py to set the correct dataset path.

2. To evaluate the entire dataset, run:
   python3 run_gasvid.py

3. To evaluate a single video, run:
   python3 owl_gasvid.py --video_id [video id]

4. Output results are stored in the gasvid_res_full directory.