Research And Implementation Of Animal Species Recognition System In Helan Mountain Based On Deep Learning

With the increasing production activities such as open-air sand and coal mining in the Helan Mountain Nature Reserve,not only has the ecological environment been greatly damaged,but the wild animals living in it also face severe survival tests.Therefore,timely and accurate identification of the wild animal species in the Helan Mountain Nature Reserve is of great significance for the sustainable development of the reserve.In the rapidly developing environment of artificial intelligence and computer vision,the recognition of wildlife species requires a large amount of image data to train models.However,it is difficult to obtain image data of corresponding wildlife in the Helan Mountain Nature Reserve,there are few existing images,and there is no high-quality Helan Mountain Nature Reserve wildlife dataset for research.At the same time,the terrain of the protected area is complex,and the living environment of wild animals is complex and diverse.Many wild animal images have issues such as lighting,occlusion,and blurring,which also have a negative impact on the accuracy and robustness of the model.In addition,there are many types of wild animals in the protected area,and there are similarities between different types,which can easily lead to misjudgment.It is also necessary to further improve the generalization ability and robustness of wild animal species recognition models.To address the above issues,the research content of this article is as follows:(1)This article independently constructed a wildlife dataset for the Helan Mountain Nature Reserve,preprocessed the images based on predefined standards,and optimized and expanded the dataset by combining various data enhancement technologies.This dataset includes 8 types of animal species,such as yaks,blue horse chickens,etc.,and basically covers the common wild animal populations in the Helan Mountain Nature Reserve.(2)Efficient feature extraction is achieved by integrating weighted bidirectional feature pyramid network(BiFPN),effective channel attention(ECA)module,and deep separable convolution;By assigning different weights to different channels or regions,the model focuses on important channel and spatial information,improves the original YOLOv5 algorithm structure,and ultimately proposes a Helan Mountain wildlife species recognition model based on deep learning.The model proposed in this paper can focus more quickly and accurately on the spatial and Semantic information that needs attention in the task of animal species recognition,and effectively improve the detection accuracy of small targets,occluded and fuzzy targets.Its mAP is 3.2%better than the benchmark YOOv5 algorithm,and its accuracy in the test set is 95.5%.(3)This article combines the improved model to design a Helan Mountain animal species recognition system,which can meet practical application needs and provide technical support and guarantee for wildlife protection work.