基于多尺度特征融合的轻量型野外蝙蝠检测  

Lightweight wild bat detection method based on multi-scale feature fusion

作　　者：王杨 马唱 胡明 孙涛[2] 饶元[3] 袁振羽 WANG Yang;MA Chang;HU Ming;SUN Tao;RAO Yuan;YUAN Zhenyu(School of Computer and Information,Anhui Normal University,Wuhu Anhui 241002,China;School of Mechanical and Electrical Engineering,Harbin Institute of Technology,Harbin Heilongjiang 150001,China;College of Information and Computer Science,Anhui Agricultural University,Hefei Anhui 230036,China)

机构地区：[1]安徽师范大学计算机与信息学院,安徽芜湖241002 [2]哈尔滨工业大学机电工程学院,黑龙江哈尔滨150001 [3]安徽农业大学信息与计算机学院,安徽合肥230036

出　　处：《图学学报》2025年第1期70-80,共11页Journal of Graphics

基　　金：国家自然科学基金(61871412);安徽省自然科学基金重点项目(KJ2019A0938,KJ2021A1314,KJ2019A0979);机器视觉检测安徽省重点实验室开放基金资助(KLMVI-2023-HIT-11);农业农村部农业传感器重点实验室开放课题(KLAS2023KF001);安徽高校自然科学重点研究项目(2022AH052899)。

摘　　要：野外蝙蝠检测对于生态保护和科学研究具有重要意义。针对计算资源有限和复杂野外环境带来的挑战,提出了一种轻量型蝙蝠检测模型(LiteDETR-Bat),旨在实现高效的实时检测。首先为了解决特征映射冗余问题,引入重参数卷积高效层聚合网络(RCELAN)替换传统的ResNet主干网络,采用多分支特征聚合机制,有效降低了计算复杂度和参数量。其次设计了动态采样多尺度特征融合(DS-MFF),该结构集成空洞卷积和动态采样算子,通过扩大感受野并自适应调整采样位置,优化多尺度特征融合,提升多样化特征处理时模型的灵活性和鲁棒性。最后在安徽省野外环境下采集了一个涵盖多种光照条件、视角变化及蝙蝠形态变化的蝙蝠数据集,并进行了模型性能等相关实验。实验结果表明,该LiteDETR-Bat模型不仅能够使参数量降低了46.5%,mAP达到97.2%,同时在准确性和实时性上相比于YOLO系列算法均取得了一定地提升。LiteDETR-Bat模型为野外蝙蝠的监测与保护工作提供了有力的技术支持,展现了其在生态监测和生物多样性保护中的应用潜力。Bat detection in the wild is crucial for ecological protection and scientific research.To address the challenges brought by limited computing resources and complex wild environments,a lightweight bat detection model(LiteDETR-Bat)was proposed to achieve efficient real-time detection.Firstly,in order to solve the problem of feature mapping redundancy,the reparameterized convolutional efficient layer aggregation network(RCELAN)was introduced,replacing the traditional ResNet backbone network and adopting a multi-branch feature aggregation mechanism,thereby reducing computational complexity and parameter quantity.Secondly,a dynamic sampling-multi scale feature fusion(DS-MFF)was designed.This structure integrated dilated convolution and dynamic sampling operators,optimizing 0multi-scale feature fusion by expanding the receptive field and adaptively adjusting sampling positions,which enhanced the flexibility and robustness of the model in processing diversified features.Finally,a bat dataset covering various lighting conditions,perspective changes,and bat morphology changes was collected in the wild environment of Anhui Province,and related experiments such as model performance were conducted on this dataset.Experimental results showed that the proposed LiteDETR-Bat model not only reduced the number of parameters by 46.5%and achieved an mAP of 97.2%,but also made certain improvements in accuracy and real-time performance compared with the YOLO series algorithms.The LiteDETR-Bat model provided strong technical support for the monitoring and protection of wild bats,and demonstrated its application potential in ecological monitoring and biodiversity conservation.

关 键 词：野外蝙蝠 RT-DETR 多尺度特征 轻量级 目标检测 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] Q95[自动化与计算机技术—计算机科学与技术]