UAS-Based Real-Time Detection of Red-Cockaded Woodpecker Cavities in Heterogeneous Landscapes Using YOLO Object Detection Algorithms

In recent years, deep learning-based approaches have proliferated across a variety of ecological studies. Inspired by deep learning’s emerging prominence as the preferred tool for analyzing wildlife image datasets, this study employed You Only Look Once (YOLO), a single-shot, real-time object detection algorithm, to effectively detect cavity trees of Red-cockaded Woodpeckers or RCW (Dryobates borealis). In spring 2022, using an unmanned aircraft system (UAS), we conducted presence surveys for RCW cavity trees within a 1264-hectare area in the Sam Houston National Forest (SHNF). Additionally, known occurrences of RCW cavity trees outside the surveyed area were aerially photographed, manually annotated, and used as a training dataset. Both YOLOv4-tiny and YOLOv5n architectures were selected as target models for training and later used for inferencing separate aerial photos from the study area. A traditional survey using the pedestrian methods was also conducted concurrently and used as a baseline survey to compare our new methods. Our best-performing model generated an mAP (mean Average Precision) of 95% and an F1 score of 85% while maintaining an inference speed of 2.5 frames per second (fps). Additionally, five unique cavity trees were detected using our model and UAS approach, compared with one unique detection using traditional survey methods. Model development techniques, such as preprocessing images with tiling and Sliced Aided Hyper Inferencing (SAHI), proved to be critical components of improved detection performance. Our results demonstrated the two YOLO architectures with tiling and SAHI strategies were able to successfully detect RCW cavities in heavily forested, heterogenous environments using semi-automated review. Furthermore, this case study represents progress towards eventual real-time detection where wildlife managers are targeting small objects. These results have implications for more achievable conservation goals, less costly operations, a safer work environment for personnel, and potentially more accurate survey results in environments that are difficult using traditional methods.