Fusion of visible and thermal images improves automated detection and classification of animals for drone surveys

Visible and thermal images acquired from drones (unoccupied aircraft systems) have substantially improved animal monitoring. Combining complementary information from both image types provides a powerful approach for automating detection and classification of multiple animal species to augment drone surveys. We compared eight image fusion methods using thermal and visible drone images combined with two supervised deep learning models, to evaluate the detection and classification of white-tailed deer (Odocoileus virginianus), domestic cow (Bos taurus), and domestic horse (Equus caballus). We classified visible and thermal images separately and compared them with the results of image fusion. Fused images provided minimal improvement for cows and horses compared to visible images alone, likely because the size, shape, and color of these species made them conspicuous against the background. For white-tailed deer, which were typically cryptic against their backgrounds and often in shadows in visible images, the added information from thermal images improved detection and classification in fusion methods from 15 to 85%. Our results suggest that image fusion is ideal for surveying animals inconspicuous from their backgrounds, and our approach uses few image pairs to train compared to typical machine-learning methods. We discuss computational and field considerations to improve drone surveys using our fusion approach. Supplemental files attached below

Fast and Accurate, Convolutional Neural Network Based Approach for Object Detection from UAV

Unmanned Aerial Vehicles (UAVs), have intrigued different people from all walks of life, because of their pervasive computing capabilities. UAV equipped with vision techniques, could be leveraged to establish navigation autonomous control for UAV itself. Also, object detection from UAV could be used to broaden the utilization of drone to provide ubiquitous surveillance and monitoring services towards military operation, urban administration and agriculture management. As the data-driven technologies evolved, machine learning algorithm, especially the deep learning approach has been intensively utilized to solve different traditional computer vision research problems. Modern Convolutional Neural Networks based object detectors could be divided into two major categories: one-stage object detector and two-stage object detector. In this study, we utilize some representative CNN based object detectors to execute the computer vision task over Stanford Drone Dataset (SDD). State-of-the-art performance has been achieved in utilizing focal loss dense detector RetinaNet based approach for object detection from UAV in a fast and accurate manner.Comment: arXiv admin note: substantial text overlap with arXiv:1803.0111

Detection of Sand Boils from Images using Machine Learning Approaches

Levees provide protection for vast amounts of commercial and residential properties. However, these structures degrade over time, due to the impact of severe weather, sand boils, subsidence of land, seepage, etc. In this research, we focus on detecting sand boils. Sand boils occur when water under pressure wells up to the surface through a bed of sand. These make levees especially vulnerable. Object detection is a good approach to confirm the presence of sand boils from satellite or drone imagery, which can be utilized to assist in the automated levee monitoring methodology. Since sand boils have distinct features, applying object detection algorithms to it can result in accurate detection. To the best of our knowledge, this research work is the first approach to detect sand boils from images. In this research, we compare some of the latest deep learning methods, Viola Jones algorithm, and other non-deep learning methods to determine the best performing one. We also train a Stacking-based machine learning method for the accurate prediction of sand boils. The accuracy of our robust model is 95.4%

Forest canopy mortality during the 2018-2020 summer drought years in Central Europe: The application of a deep learning approach on aerial images across Luxembourg

Efficient monitoring of tree canopy mortality requires data that cover large areas and capture changes over time while being precise enough to detect changes at the canopy level. In the development of automated approaches, aerial images represent an under-exploited scale between high-resolution drone images and satellite data. Our aim herein was to use a deep learning model to automatically detect canopy mortality from high-resolution aerial images after severe drought events in the summers 2018–2020 in Luxembourg. We analysed canopy mortality for the years 2017–2020 using the EfficientUNet++, a state-of-the-art convolutional neural network. Training data were acquired for the years 2017 and 2019 only, in order to test the robustness of the model for years with no reference data. We found a severe increase in canopy mortality from 0.64 km2 in 2017 to 7.49 km2 in 2020, with conifers being affected at a much higher rate than broadleaf trees. The model was able to classify canopy mortality with an F1-score of 66%–71% and we found that for years without training data, we were able to transfer the model trained on other years to predict canopy mortality, if illumination conditions did not deviate severely. We conclude that aerial images hold much potential for automated regular monitoring of canopy mortality over large areas at canopy level when analysed with deep learning approaches. We consider the suggested approach a cost-efficient and -effective alternative to drone and field-based sampling