146292 - Coconut trees detection and segmentation in aerial imagery using mask region-based convolution neural network.pdf (3.03 MB)
Coconut trees detection and segmentation in aerial imagery using mask region-based convolution neural network
journal contribution
posted on 2023-05-21, 01:58 authored by Iqbal, MS, Ali, H, Son TranSon Tran, Iqbal, TFood resources face severe damages under extraordinary situations of catastrophes such as earthquakes, cyclones, and tsunamis. Under such scenarios, speedy assessment of food resources from agricultural land is critical as it supports aid activity in the disaster-hit areas. In this article, a deep learning approach was presented for the detection and segmentation of coconut trees in aerial imagery provided through the AI competition organised by the World Bank in collaboration with OpenAerialMap and WeRobotics. Masked Region-based Convolution Neural Network (Mask R-CNN) approach was used for identification and segmentation of coconut trees. For the segmentation task, Mask R-CNN model with ResNet50 and ResNet101 based architectures was used. Several experiments with different configuration parameters were performed and the best configuration for the detection of coconut trees with more than 90% confidence factor was reported. For the purpose of evaluation, Microsoft COCO dataset evaluation metric namely mean average precision (mAP) was used.An overall 91% mean average precision for coconut trees’ detection was achieved.
History
Publication title
IET Computer VisionVolume
15Issue
6Pagination
428-439ISSN
1751-9632Department/School
School of Information and Communication TechnologyPublisher
Wiley-Blackwell Publishing LtdPlace of publication
United KingdomRights statement
© 2021 The Authors. IET Computer Vision published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. This is an open access article under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License, (https://creativecommons.org/licenses/by/4.0/) which permits use, distribution and reproduction in any medium, provided the original work is properly cited.Repository Status
- Open