# Fine-tuned Pre-trained Mask R-CNN Models for Surface Object Detection Source: [Fine-tuned Pre-trained Mask R-CNN Models for Surface Object Detection]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:39:59.706Z Authors: Haruhiro Fujita, Masatoshi Itagaki, Kenta Ichikawa, Yew Kwang Hooi, Kazutaka Kawano, Ryo Yamamoto Published: 2020-10-22T06:09:59.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()