Tue, Aug 16, 2022: 5:00 PM-6:30 PM
ESA Exhibit Hall
Background/Question/MethodsOne of the most challenging problems faced by ecologists and other biological researchers today is to analyze the massive amounts of data being collected by advanced monitoring systems like camera traps, wireless sensor networks, high-frequency radio trackers, global positioning systems, and satellite tracking systems being used today. It has become expensive, laborious, and time-consuming to analyze this huge data using manual and traditional statistical techniques. Recent developments in the deep learning field are showing promising results towards automating the analysis of these extremely large datasets.The primary objective of this study was to test the capabilities of the state-of-the-art deep learning architectures to detect birds in the webcam captured images. A total of 10592 images were collected for this study from the Cornell Lab of Ornithology live stream feeds situated in six unique locations in United States, Ecuador, New Zealand, and Panama. To achieve the main objective of the study, we studied and evaluated two convolutional neural network object detection meta-architectures, single-shot detector (SSD) and Faster R-CNN in combination with MobileNet-V2, ResNet50, ResNet101, ResNet152, and Inception ResNet-V2 feature extractors. Through transfer learning, all the models were initialized using weights pre-trained on the MS COCO dataset.
Results/ConclusionsThe Faster R-CNN model coupled with ResNet152 outperformed all other models with a mean average precision of 92.3\%. However, the SSD model with the MobileNet-V2 feature extraction network achieved the lowest inference time (110ms) and the smallest memory capacity (30.5MB) compared to its counterparts. The outstanding results achieved in this study confirm that deep learning-based algorithms are capable of detecting birds of different sizes in different environments and the best model could potentially help ecologists in monitoring and identifying birds from other species.
Results/ConclusionsThe Faster R-CNN model coupled with ResNet152 outperformed all other models with a mean average precision of 92.3\%. However, the SSD model with the MobileNet-V2 feature extraction network achieved the lowest inference time (110ms) and the smallest memory capacity (30.5MB) compared to its counterparts. The outstanding results achieved in this study confirm that deep learning-based algorithms are capable of detecting birds of different sizes in different environments and the best model could potentially help ecologists in monitoring and identifying birds from other species.