Automated Detection of Koalas with Deep Learning Ensembles

Abstract

Effective management of threatened and invasive species requires regular and reliable population estimates. Drones are increasingly utilised by ecologists for this purpose as they are relatively inexpensive. They enable larger areas to be surveyed than traditional methods for many species, particularly cryptic species such as koalas, with less disturbance. The development of robust and accurate methods for species detection is required to effectively use the large volumes of data generated by this survey method. The enhanced predictive and computational power of deep learning ensembles represents a considerable opportunity to the ecological community. In this study, we investigate the potential of deep learning ensembles built from multiple convolutional neural networks (CNNs) to detect koalas from low-altitude, drone-derived thermal data. The approach uses ensembles of detectors built from combinations of YOLOv5 and models from Detectron2. The ensembles achieved a strong balance between probability of detection and precision when tested on ground-truth data from radio-collared koalas. Our results also showed that greater diversity in ensemble composition can enhance overall performance. We found the main impediment to higher precision was false positives but expect these will continue to reduce as tools for geolocating detections are improved. The ability to construct ensembles of different sizes will allow for improved alignment between the algorithms used and the characteristics of different ecological problems. Ensembles are efficient and accurate and can be scaled to suit different settings, platforms and hardware availability, making them capable of adaption for novel applications.

A Novel Scouring Method to Monitor Nocturnal Mammals Using Uncrewed Aerial Vehicles and Thermal Cameras—A Comparison to Line Transect Spotlight Counts

“…Recent technological advances, such as better battery capacity, improved sensors, and lower cost, have accelerated the applications of aerial drones, also known as uncrewed or unmanned aerial vehicles (UAV), unmanned aerial systems (UAS) and remotely piloted aircraft systems (RPAS), in the field of ecology and wildlife monitoring [22][23][24][25][26][27][28][29][30]. Especially multirotor drones, as opposed to fixed-winged drones, equipped with thermal infrared (TIR) cameras have proven to be a useful tool for monitoring cryptic and nocturnal species, on par with spotlight methods [3,25,26,[30][31][32][33][34][35][36][37][38]. In a review, Linchant et al [30] pointed out that several studies have examined the rising potential of the use of drones for wildlife monitoring, but these have primarily been conducted in classic line transects with the sensors pointed directly downwards, so there exists a need to explore and develop new methods.…”

Using YOLO Object Detection to Identify Hare and Roe Deer in Thermal Aerial Video Footage—Possible Future Applications in Real-Time Automatic Drone Surveillance and Wildlife Monitoring

“…Over the past decade, artificial intelligence has led to significant progress in the domain of computer vision, automating image and video analysis tasks. Among computer vision methods, Convolutional Neural Networks (CNNs) are particularly promising for future advances in automating wildlife monitoring [6,[12][13][14][15][16][17][18]. Corcoran et al [3] concluded that when implementing automatic detection, fixed-winged drones with RGB sensors were ideal for detecting larger animals in open terrain, whereas, for small, elusive animals in more complex habitats, multi-rotor systems with infrared (IR) or thermal infrared sensors are the better choice, especially when monitoring cryptic and nocturnal animals.…”

“…A popular and open-source group of CNNs is the YOLO (You Only Look Once) object detection and image segmentation models, with several iterations and active development [14,[19][20][21][22], and a technology cross-fusion with drones has already been proposed as YOLO-Based UAV Technology (YBUT) [6]. The advantages of the YOLO models are that they are fast [8], making it possible to perform object detection in real-time on live footage, and that they are relatively user-friendly and intuitive, making the models approachable to non-computer scientists.…”

“…Which model to choose depends on the choice in hardware and application, but the differences in performance between the models are minor compared to the importance of the quality of the training datasets [27]. Several studies have combined aerial footage analysis with the use of machine learning [4,6,8,12,14,15,25,[32][33][34][35][36][37], and the increased use of drones for wildlife surveys highlights the need for automation when analyzing imagery. This opens up possibilities of combining real-time object detection and tracking with commercial drone technology.…”