Weakly supervised pseudo label generation for construction vehicle segmentation

Segmentation tasks in computer vision have been adopted in various studies in the civil engineering domain to provide accurate object locations in images. However, preparing an-notation to train segmentation models is a time consuming and costly process, which hinders the use of segmentation models in vision-based applications. To address the problem, this study proposes a fusion model integrating self-supervised equivariant attention mechanism (SEAM) and sub-category exploration (SC-CAM) to generate pseudo labels in the form of polygon annotation from bounding box annotation that is relatively easy to obtain. To test the performance of the fusion model, a public data set - Advanced Infrastructure Management Group (AIM) dataset - for construction object detection was selected to generate pseudo labels; the effec-tiveness of pseudo labels was measured by the segmentation performance of a feature pyramid network (FPN) trained with the pseudo labels. FPN showed the mean intersection over union (mIoU) score of 86.03%, demonstrating the po-tential of the proposed fusion model to reduce the manual annotation efforts in preparing training data for segmenta-tion models.