Coconut trees detection and segmentation in aerial imagery using mask region-based convolution neural network
This work addresses the need for rapid agricultural assessment in disaster-hit areas, but it is incremental as it applies an existing method to a new dataset.
The paper tackled the problem of detecting and segmenting coconut trees in aerial imagery to assess food resources after disasters, achieving 91% mean average precision (mAP) for detection with over 90% confidence.
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.