Optimization Research of Bird Detection Algorithm Based on YOLO in Deep Learning Environment

Abstract

Recent environmental degradation has led to an unparalleled decline in wild bird habitats, resulting in a worldwide decrease in bird populations. To prevent extinction, it is vital to implement protective measures. One effective solution could be the application of deep learning techniques to identify bird species and habitats, which would prove useful for bird enthusiasts and rescuers. Therefore, a dataset of 20 globally prized bird species has been collated and analyzed. The Bird-YOLO algorithm precisely identifies avian creatures by combining neural architecture search and knowledge distillation. To diminish noise interference, preprocessing of images and dimension clustering of prior boxes are carried out prior to the training. The experiments show that the Bird-YOLO algorithm attains an 88.23% bird recognition rate, with a frames per second (FPS) of 47.