Coconut trees detection and segmentation in aerial imagery using mask region-based convolution neural network

TL;DR

This paper presents a deep learning method using Mask R-CNN with ResNet architectures for accurate detection and segmentation of coconut trees in aerial imagery, achieving over 90% confidence and 91% mAP.

Contribution

It introduces a novel application of Mask R-CNN architectures for coconut tree detection in aerial images, optimized through experiments for high accuracy.

Findings

Over 90% confidence in detection

Achieved 91% mean average precision

Effective segmentation of coconut trees

Abstract

Food 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 is presented for the detection and segmentation of coconut tress in aerial imagery provided through the AI competition organized by the World Bank in collaboration with OpenAerialMap and WeRobotics. Maked Region-based Convolutional Neural Network approach was used identification and segmentation of coconut trees. For the segmentation task, Mask R-CNN model with ResNet50 and ResNet1010 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.