Yaqi Cai

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.