Jaringan sarafsyaraf konvolusitiruan berbasis wilayah telah digunakan untuk melacak objek dari kamera yang dipasang di [[pesawat nirawak]],<ref>{{Cite news|last=Nene|first=Vidi|url=https://dronebelow.com/2019/08/02/deep-learning-based-real-time-multiple-object-detection-and-tracking-via-drone/|title=Deep Learning-Based Real-Time Multiple-Object Detection and Tracking via Drone|date=2 Agustus 2019|work=Drone Below|access-date=28 Maret 2020}}</ref> locating text in an image,<ref>{{Cite news|last=Ray|first=Tiernan|url=https://www.zdnet.com/article/facebook-pumps-up-character-recognition-to-mine-memes/|title=Facebook pumps up character recognition to mine memes|date=Sep 11, 2018 |publisher=[[ZDNET]] |access-date=Mar 28, 2020}}</ref> dan memungkinkan pendeteksian objek di [[Google Lens]].<ref>{{Cite news|last=Sagar|first=Ram|url=https://analyticsindiamag.com/these-machine-learning-techniques-make-google-lens-a-success/|title=These machine learning methods make google lens a success|date=Sep 9, 2019|work=Analytics India|access-date=Mar 28, 2020}}</ref> Mask R-CNN berfungsi sebagai salah satu dari tujuh tugas dalam MLPerf Training Benchmark, yang merupakan kompetisi untuk mempercepat pelatihan jaringan saraf.<ref>{{cite arXiv|eprint=1910.01500v3|class=math.LG|first=Peter|last=Mattson|title=MLPerf Training Benchmark|date=2019|display-authors=etal}}</ref>
