Datasets:
Tasks:
Image Segmentation
Modalities:
Image
Languages:
English
Size:
1K<n<10K
Tags:
methane-detection
thermal-infrared
agriculture
semantic-segmentation
optical-gas-imaging
environmental-monitoring
License:
File size: 3,887 Bytes
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{
"name": "Controlled Diet (CD) Dataset for Methane Plume Detection",
"version": "1.0.0",
"description": "A large-scale collection of 4,885 methane (CH₄) plume images captured using optical gas imaging (OGI) technology for semantic segmentation tasks",
"authors": [
{
"name": "Mohamed G. Embaby",
"affiliation": "Southern Illinois University Carbondale",
"email": "embaby@siu.edu"
},
{
"name": "Toqi Tahamid Sarker",
"affiliation": "Southern Illinois University Carbondale",
"email": "toqitahamid.sarker@siu.edu"
},
{
"name": "Amer AbuGhazaleh",
"affiliation": "Southern Illinois University Carbondale",
"email": "aamer@siu.edu"
},
{
"name": "Khaled R. Ahmed",
"affiliation": "Southern Illinois University Carbondale",
"email": "kahmed@siu.edu"
}
],
"license": "CC0-1.0",
"publication": {
"title": "Optical gas imaging and deep learning for quantifying enteric methane emissions from rumen fermentation in vitro",
"journal": "IET Image Processing",
"year": 2025,
"doi": "10.1049/ipr2.13327",
"url": "https://doi.org/10.1049/ipr2.13327"
},
"funding": {
"agency": "National Institute of Food and Agriculture, United States Department of Agriculture",
"award_number": "2022-70001-37404"
},
"dataset_info": {
"total_images": 4885,
"image_resolution": "640x480",
"file_format": "PNG",
"camera": "FLIR GF77 OGI camera",
"spectral_range": "7-8.5 μm",
"annotation_type": "semantic segmentation masks",
"classes": 4,
"background_method": "ice block thermal contrast"
},
"splits": {
"train": {
"images": 3905,
"percentage": 80
},
"validation": {
"images": 496,
"percentage": 10
},
"test": {
"images": 484,
"percentage": 10
}
},
"class_distribution": {
"class_1": {
"gc_range_ppm": "166-171",
"diet": "Control (50:50 F:C ratio)",
"train": 1079,
"validation": 138,
"test": 133,
"total": 1350
},
"class_2": {
"gc_range_ppm": "300-334",
"diet": "Low Forage (20:80 F:C ratio)",
"train": 1268,
"validation": 162,
"test": 157,
"total": 1587
},
"class_3": {
"gc_range_ppm": "457-510",
"diet": "High Forage (80:20 F:C ratio)",
"train": 1558,
"validation": 196,
"test": 194,
"total": 1948
}
},
"experimental_setup": {
"source": "In vitro continuous culture fermentation system",
"simulation": "Cow rumen environment",
"collection_method": "24-hour ANKOM batch culture",
"validation_methods": ["Gas Chromatography (GC)", "Laser Methane Detector (LMD)"],
"temperature": "22°C controlled room temperature"
},
"mask_generation": {
"method": "Automated pipeline",
"steps": [
"Background subtraction using pre-recorded reference frames",
"Contrast enhancement for improved plume visibility",
"Adaptive thresholding for binary separation",
"Watershed algorithm with Sobel filter elevation maps",
"Region analysis with size-based filtering",
"Binary mask generation for pixel-wise annotations"
]
},
"applications": [
"Semantic segmentation model training",
"Agricultural monitoring and assessment",
"Environmental research on livestock emissions",
"Computer vision system development",
"Climate change mitigation strategy evaluation"
],
"keywords": [
"optical gas imaging",
"methane detection",
"semantic segmentation",
"livestock emissions",
"computer vision",
"deep learning",
"agriculture",
"climate change",
"FLIR GF77",
"rumen fermentation"
],
"created": "2025-01-19",
"updated": "2025-01-19",
"format_version": "1.0",
"schema": "https://schema.org/Dataset"
} |