RTAD: A Real-Time Animal Object Detection Model Based on a Large Selective Kernel and Channel Pruning

Animal resources are significant to human survival and development and the ecosystem balance. Automated multi-animal object detection is critical in animal research and conservation and ecosystem monitoring. The objective is to design a model that mitigates the challenges posed by the large number o...

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Bibliographic Details
Published in:Information (Basel) 2023-10, Vol.14 (10), p.535
Main Authors: Liu, Sicong, Fan, Qingcheng, Zhao, Chunjiang, Li, Shuqin
Format: Article
Language:English
Subjects:
Accuracy
Animals
Automation
channel pruning
Computer networks
Ecological monitoring
Fisher information
Kernels
LSK module
Mathematical models
Methods
Neural networks
Object recognition
Parameters
Protection and preservation
Pruning
Real time
real-time animal object detection
Telecommunication systems
Telematics
vision transformer
Wildlife conservation
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Summary:Animal resources are significant to human survival and development and the ecosystem balance. Automated multi-animal object detection is critical in animal research and conservation and ecosystem monitoring. The objective is to design a model that mitigates the challenges posed by the large number of parameters and computations in existing animal object detection methods. We developed a backbone network with enhanced representative capabilities to pursue this goal. This network combines the foundational structure of the Transformer model with the Large Selective Kernel (LSK) module, known for its wide receptive field. To further reduce the number of parameters and computations, we incorporated a channel pruning technique based on Fisher information to eliminate channels of lower importance. With the help of the advantages of the above designs, a real-time animal object detection model based on a Large Selective Kernel and channel pruning (RTAD) was built. The model was evaluated using a public animal dataset, AP-10K, which included 50 annotated categories. The results demonstrated that our model has almost half the parameters of YOLOv8-s yet surpasses it by 6.2 AP. Our model provides a new solution for real-time animal object detection.
ISSN:2078-2489
2078-2489
DOI:10.3390/info14100535