A Fine-Grained Recognition Neural Network with High-Order Feature Maps via Graph-Based Embedding for Natural Bird Diversity Conservation

Abstract

The conservation of avian diversity plays a critical role in maintaining ecological balance and ecosystem function, as well as having a profound impact on human survival and livelihood. With species’ continuous and rapid decline, information and intelligent technology have provided innovative knowledge about how functional biological diversity interacts with environmental changes. Especially in complex natural scenes, identifying bird species with a real-time and accurate pattern is vital to protect the ecological environment and maintain biodiversity changes. Aiming at the fine-grained problem in bird image recognition, this paper proposes a fine-grained detection neural network based on optimizing the YOLOV5 structure via a graph pyramid attention convolution operation. Firstly, the Cross Stage Partial (CSP) structure is introduced to a brand-new backbone classification network (GPA-Net) for significantly reducing the whole model’s parameters. Then, the graph pyramid structure is applied to learn the bird image features of different scales, which enhances the fine-grained learning ability and embeds high-order features to reduce parameters. Thirdly, YOLOV5 with the soft non-maximum suppression (NMS) strategy is adopted to design the detector composition, improving the detection capability for small targets. Detailed experiments demonstrated that the proposed model achieves better or equivalent accuracy results, over-performing current advanced models in bird species identification, and is more stable and suitable for practical applications in biodiversity conservation.

Deep Convolutional Neural Network for Automated Bird Species Classification

“…Deep Learning-Based Methods: With the usage of deep gaining knowledge of-based strategies, the categorization of chook species has made extensive development. The paper 'A fine-grained recognition neural network' used deep gaining knowledge of the strategies of a fine-grained bird recognition method based on a graph attention pyramid to solve the fine-grained problem with bird image recognition [16]. The use of deep gaining knowledge of for problems requiring fine-grained categorization, consisting of bird species class, is validated in this approach.…”

Automatic Bird Species Recognition from Images with Feature Enhancement and Contrastive Learning

“…Within the realm of deep learning, two primary approaches have emerged: strongly supervised and weakly supervised classification [14,15]. Strongly supervised methods, while achieving high accuracy, demand a significant investment of time and effort in meticulously annotating training data [16,17]. These annotations typically encompass not only the bird species label but also detailed information such as bounding boxes around the bird and annotations for various body parts [18].…”