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An Efficient Method for Monitoring Birds Based on Object Detection and Multi-Object Tracking Networks

Chen, Xian ; Pu, Hongli ; He, Yihui ; Lai, Mengzhen ; Zhang, Daike ; Chen, Junyang ; Pu, Haibo

Animals (Basel), 2023-05, Vol.13 (10), p.1713 [Periódico revisado por pares]

Switzerland: MDPI AG

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Título:
An Efficient Method for Monitoring Birds Based on Object Detection and Multi-Object Tracking Networks
Autor: Chen, Xian ; Pu, Hongli ; He, Yihui ; Lai, Mengzhen ; Zhang, Daike ; Chen, Junyang ; Pu, Haibo
Assuntos: Algorithms ; Animal behavior ; Animals ; Aquatic birds ; Artificial intelligence ; attention mechanism ; Automation ; Biodiversity ; bird conservation ; Birds ; Classification ; Computer vision ; Conservation ; Counting ; Datasets ; Ecological balance ; Efficiency ; Endangered & extinct species ; Endangered animals ; Endangered species ; Feasibility ; Information dissemination ; Machine vision ; Methods ; Monitoring ; Monitoring methods ; multi-object tracking ; Multiple target tracking ; Natural areas ; Nature conservation ; Nature reserves ; Neural networks ; object detection ; Performance evaluation ; Protection and preservation ; Technology application ; Tracking networks ; Unmanned aerial vehicles ; Wetlands ; Wildlife conservation ; YOLOv7
É parte de: Animals (Basel), 2023-05, Vol.13 (10), p.1713
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Descrição: To protect birds, it is crucial to identify their species and determine their population across different regions. However, currently, bird monitoring methods mainly rely on manual techniques, such as point counts conducted by researchers and ornithologists in the field. This method can sometimes be inefficient, prone to errors, and have limitations, which may not always be conducive to bird conservation efforts. In this paper, we propose an efficient method for wetland bird monitoring based on object detection and multi-object tracking networks. First, we construct a manually annotated dataset for bird species detection, annotating the entire body and head of each bird separately, comprising 3737 bird images. We also built a new dataset containing 11,139 complete, individual bird images for the multi-object tracking task. Second, we perform comparative experiments using a state-of-the-art batch of object detection networks, and the results demonstrated that the YOLOv7 network, trained with a dataset labeling the entire body of the bird, was the most effective method. To enhance YOLOv7 performance, we added three GAM modules on the head side of the YOLOv7 to minimize information diffusion and amplify global interaction representations and utilized Alpha-IoU loss to achieve more accurate bounding box regression. The experimental results revealed that the improved method offers greater accuracy, with mAP@0.5 improving to 0.951 and mAP@0.5:0.95 improving to 0.815. Then, we send the detection information to DeepSORT for bird tracking and classification counting. Finally, we use the area counting method to count according to the species of birds to obtain information about flock distribution. The method described in this paper effectively addresses the monitoring challenges in bird conservation.
Editor: Switzerland: MDPI AG
Idioma: Inglês

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An Efficient Method for Monitoring Birds Based on Object Detection and Multi-Object Tracking Networks

Chen, Xian ; Pu, Hongli ; He, Yihui ; Lai, Mengzhen ; Zhang, Daike ; Chen, Junyang ; Pu, Haibo

Switzerland: MDPI AG

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