Abstract: Piglet crushing by sows is one of the primary causes of piglet mortality before weaning. The occlusion of piglets by sows can make it highly challenging to detect the crushing events using existing computer vision. In this study, an accurate and rapid detection of piglet crushing events was proposed using amodal instance segmentation (AIS). Firstly, an improved AIS model, BCNet-FF (bilayer convolutional network-focused fusion), was developed by enhancing the baseline bilayer convolutional network (BCNet). The FLA-DSC (focused linear attention-depthwise separable convolution) module, an enhanced version with focused linear attention was integrated into the backbone network in order to improve the feature extraction for the occluded objects. Additionally, the FreqFusion feature fusion enhancement module was introduced into the feature pyramid networks (FPN), which strengthened multi-scale feature representation and enhanced feature extraction and fusion for occluded object contours. Consequently, high-precision segmentation was achieved. Secondly, a custom dataset was constructed, and the video sequences were processed using BCNet-FF. Sow posture information and pig instance segmentation results was extracted. Among them, the dorsal region of the sow was detected when the posture of the sow was identified as the lateral lying. A regression equation was formulated to estimate the actual contact contour between the sow and the ground. According to this contour, a risk region was delineated to highlight the areas with a high likelihood of piglet crushing. Thirdly, a multi-object tracking algorithm was applied using the target mask intersection over union (IoU) between adjacent frames. The piglets within the risk region were tracked. Finally, the risk region overlap ratio of piglets was calculated to determine whether a crushing event had occurred. The results show that the BCNet-FF was achieved in the precision rates of 97.8%, 98.2%, and 99.5% for the standing, sternal lying, and lateral lying postures, respectively, with the recall rates of 98.2%, 97.2%, and 97.8%, respectively. Segmentation ablation experiments showed that there was an improvement of 2.2 percentage points in the mAP@50, 2.5 percentage points in the mIoU, and an increase of 3.1 percentage points in the AP@50 for piglets using BCNet-FF, compared with the BCNet. The model size increased by 47.5 MB, and the inference speed was 11.4 frames per second. In the comparative experiments, BCNet-FF demonstrated superior performance in instance segmentation of sows and piglets, achieving an AP@50 of 97.4% in piglet segmentation, thereby exceeding Mask R-CNN, CenterMask2, Transfiner, YOLOv11x-seg, and BCNet by 7.8, 4.6, 5.6, 2.3, and 3.1 percentage points, respectively. In the evaluation of 90 video samples, the multi-object tracking method for piglets achieved an average IDF1 of 94.5% and MOTA of 93.8%. The proposed method achieved an accuracy of 91.1% in detecting piglet crushing events, with a sensitivity of 90.6%, a specificity of 91.9%, and an average inference speed of 8.9 frames per second. The better performance of the BCNet-FF model was highlighted to segment the sows and piglets in the occlusion scenarios, particularly for the segmentation accuracy of occluded piglet masks. The multi-object tracking with the target mask IoU can be expected to accurately track the occluded piglets. The constructed regression equation and risk region enabled accurate differentiation between occluded and crushed piglets, which significantly improved piglet crushing event detection accuracy. The practicality and reliability can be achieved to detect the piglet crushing events. The finding can provide an effective approach to monitor the piglet crushing events using computer vision.