KP-Mask2Former: A fish occlusion contour recovery method based on KANsformer and PCBAM

Recovering complete contours of occluded fish is crucial for accurate fish size estimation and automated counting. Nevertheless, frequent and severe occlusions in high-density aquaculture environments make reliable contour recovery particularly challenging. To address this problem, this study proposes KP-Mask2Former, a fish contour recovery framework specifically designed for occlusion-intensive scenarios. First, a Parallel Convolutional Block Attention Module (PCBAM) is introduced into the patch-merging stages of the backbone to strengthen the representation of fish contour features. Second, Swin KANsformer is incorporated into the backbone to enhance the reconstruction of incomplete boundaries caused by occlusion. Third, a SEAM-Mask Head is developed to suppress interference from occluding instances and improve the recovery of complete fish contours. The proposed framework is evaluated on a self-constructed Occluded Oplegnathus punctatus Dataset (OOD). Experimental results demonstrate that KP-Mask2Former achieves an AP of 87.1%, representing a 4.3-percentage-point improvement over the baseline. It also outperforms representative state-of-the-art methods, including PolySnake, E2EC, PatchDCT, BCNet, Transfiner, MP-Former, and FastInst. Further analyses confirm the superiority of the proposed method in boundary delineation, intra-class occlusion handling, and multi-scale instance segmentation.