# SAM2

Inspect Any Crack converts crack segmentation masks into millimeter-scale widths and a continuous severity score, giving 7.33% mean relative width error at 41 caliper-referenced locations so inspection images yield condition judgments that can be checked against standards.

Inspect Any Crack converts crack segmentation masks into millimeter-scale widths and a continuous severity score, giving 7.33% mean relative width error at 41 caliper-referenced locations so inspection images yield condition judgments that can be checked against standards.

Total burn surface area is now regressed directly from frontal and dorsal whole-body and burn-region binary masks, cutting error below the direct pixel-area ratio baseline on a synthetic set of 5,000 human models and 320,000 rendered views.

Total burn surface area is now regressed directly from frontal and dorsal whole-body and burn-region binary masks, cutting error below the direct pixel-area ratio baseline on a synthetic set of 5,000 human models and 320,000 rendered views.

Universal object localization in X-ray security screening has been limited by scarce expert annotation, forcing reliance on RGB-pretrained segmentation models that falter on overlapping, weakly bounded baggage scans. LAO-X mines object instances from unlabeled X-ray scans, recomposes them in the absorbance domain under the Beer-Lambert law to build cluttered scenes with holistic and part pseudo-masks, then fine-tunes a SAM2 localizer through an occlusion-controlled curriculum. Across six X-ray benchmarks, it raises category-agnostic mAP over SAM2 and X-ray-specific baselines by 2% to 23% with no human-annotated labels, reaching 53.33% mAP on DET-COMPASS.

Universal object localization in X-ray security screening has been limited by scarce expert annotation, forcing reliance on RGB-pretrained segmentation models that falter on overlapping, weakly bounded baggage scans. LAO-X mines object instances from unlabeled X-ray scans, recomposes them in the absorbance domain under the Beer-Lambert law to build cluttered scenes with holistic and part pseudo-masks, then fine-tunes a SAM2 localizer through an occlusion-controlled curriculum. Across six X-ray benchmarks, it raises category-agnostic mAP over SAM2 and X-ray-specific baselines by 2% to 23% with no human-annotated labels, reaching 53.33% mAP on DET-COMPASS.

The authors built a non-invasive, high-content home-cage phenotyping framework combining SLEAP.ai markerless multi-animal pose estimation, DeepOF automated classification of autonomous and social behaviors, and SAM2 segmentation and quantification of nest-building material, and used long-term monitoring of the early infantile epileptic encephalopathy (EIEE5) model SPTAN1 R1098Q to show impaired spatial habituation in mutant-mutant pairs, persistent unreciprocated trailing of cage mates, and a pronounced reduction in nest-building, while co-housing with a wild-type littermate attenuated mutant hyperactivity and restored a more typical rest pattern.

The authors built a non-invasive, high-content home-cage phenotyping framework combining SLEAP.ai markerless multi-animal pose estimation, DeepOF automated classification of autonomous and social behaviors, and SAM2 segmentation and quantification of nest-building material, and used long-term monitoring of the early infantile epileptic encephalopathy (EIEE5) model SPTAN1 R1098Q to show impaired spatial habituation in mutant-mutant pairs, persistent unreciprocated trailing of cage mates, and a pronounced reduction in nest-building, while co-housing with a wild-type littermate attenuated mutant hyperactivity and restored a more typical rest pattern.