Detector Algorithms of Bounding Box and Segmentation Mask of a Mask R-CNN Model
This work addresses performance discrepancies in object detection for computer vision applications, but it is incremental as it focuses on analyzing existing methods rather than introducing new ones.
The paper evaluated detection performances of bounding box and segmentation mask outputs in Mask R-CNN models, finding that bounding boxes consistently outperformed segmentation masks, with significantly lower harmonic values for specific classes like linear cracks, joints, fillings, and shadows.
Detection performances on bounding box and segmentation mask outputs of Mask R-CNN models are evaluated. There are significant differences in detection performances of bounding boxes and segmentation masks, where the former is constantly superior to the latter. Harmonic values of precisions and recalls of linear cracks, joints, fillings, and shadows are significantly lower in segmentation masks than bounding boxes. Other classes showed similar harmonic values. Discussions are made on different performances of detection metrics of bounding boxes and segmentation masks focusing on detection algorithms of both detectors.