Fine-tuned Pre-trained Mask R-CNN Models for Surface Object Detection
This is an incremental improvement for detecting surface objects in road and archaeological contexts.
The study tackled road surface object detection by fine-tuning pre-trained Mask R-CNN models, resulting in notable true positive increases in bounding box detection but almost no changes in segmentation masks, with substantial false negatives observed.
This study evaluates road surface object detection tasks using four Mask R-CNN models as a pre-study of surface deterioration detection of stone-made archaeological objects. The models were pre-trained and fine-tuned by COCO datasets and 15,188 segmented road surface annotation tags. The quality of the models were measured using Average Precisions and Average Recalls. Result indicates substantial number of counts of false negatives, i.e. left detection and unclassified detections. A modified confusion matrix model to avoid prioritizing IoU is tested and there are notable true positive increases in bounding box detection, but almost no changes in segmentation masks.