Detection of Birds in the Wild using Deep Learning Methods

Object detection and localization is one of the prominent applications of the computer vision. The paper presents a comparative study of state of the art deep learning methods-YOLOv2, YOLOv3 and Mask R-CNN, for detection of birds in the wild. Detection of birds is an important problem across multiple applications including the aviation safety, avian protection and ecological science of migrant bird species. Deep learning based methods are very pre-eminent at detecting and localizing the birds in the image as it can tackle the conditions wherein the birds shown are diverse in shapes and sizes and most importantly the complex backgrounds they are in. We used the training and testing dataset provided by the NCVPRIG (BROID) conference which contained 325 and 275 images respectively. For training, we used the pre-trained models on the VOC 2012 and COCO dataset and trained them on the 325 images. We used F-score as one of the performance metrics, and F-Scores were 0.8140, 0.8721, 0.8688 for the YOLOv2, YOLOv3 and Mask R-CNN respectively. The results show that YOLOv3 outperforms YOLOv2 and is a marginal improvement over Mask R-CNN.