Work

Maskless Selective Object Removal via a Dual-Pipeline Framework with SAM–SDXL and LaMa

Year: 2026

Type: article

Abstract: Object removal is a widely used AI-eraser operation in photo editing and privacy protection, yet conventional workflows make the user paint the removal region by hand—a burden that is most acute when an image contains several objects of the same class and only one is to be erased. We present two pipelines for maskless selective removal that delete a designated object among many, using only a target ID, box, or point. Both are built around a selection mechanism: the detected candidates are indexed, and only the mask of the designated target—combined by a logical-OR when several are chosen—is constructed and inpainted, unlike the standard use of an inpainting model, which merely fills a mask that is already given. On the multi-object GQA-Inpaint benchmark, we compare the generative YOLO–SAM–SDXL (YSS) and the lightweight YOLO/MobileSAM–LaMa (YML) against PowerPaint and Inpaint-Anything. With no mask provided, the proposed pipelines select and remove the target far more accurately and reliably than the baseline (for YML, Selection-IoU 0.626 vs. 0.327 and residual object score 0.228 vs. 0.646), while their full-image quality stays within the baseline range. YML is the most reliable remover and far faster (1.31 s per image on GPU vs. 29.60 s for YSS, and 32× faster than a diffusion pipeline on CPU), whereas YSS reaches the most natural perceptual quality at the cost of a higher residual. The two are thus complementary options, chosen according to the desired speed and restoration character.

Source: AI

DOI: 10.3390/ai7080284