RTAD:基于大选择核和通道修剪的实时动物目标检测模型

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Sicong Liu, Qingcheng Fan, Chunjiang Zhao, Shuqin Li
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引用次数: 0

摘要

动物资源对人类生存发展和生态系统平衡具有重要意义。自动化多动物目标检测在动物研究、保护和生态系统监测中至关重要。目的是设计一个模型,以减轻现有动物目标检测方法中大量参数和计算所带来的挑战。为了实现这一目标,我们开发了一个具有增强代表功能的骨干网络。该网络结合了Transformer模型的基本结构和大选择内核(Large Selective Kernel, LSK)模块,后者以其广泛的接受域而闻名。为了进一步减少参数数量和计算量,我们采用了基于Fisher信息的信道修剪技术来消除较低重要性的信道。利用以上设计的优点,建立了基于大选择核和通道修剪(RTAD)的实时动物目标检测模型。该模型使用公共动物数据集AP-10K进行评估,该数据集包括50个带注释的类别。结果表明,该模型的参数几乎只有YOLOv8-s的一半,但却超过了YOLOv8-s的6.2 AP,为实时动物目标检测提供了新的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 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.
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来源期刊
Information (Switzerland)
Information (Switzerland) Computer Science-Information Systems
CiteScore
6.90
自引率
0.00%
发文量
515
审稿时长
11 weeks
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