两阶段群体智能集成深度迁移学习（SI-EDTL）用于无人机车辆检测
Two-Stage Swarm Intelligence Ensemble Deep Transfer Learning (SI-EDTL) for Vehicle Detection Using Unmanned Aerial Vehicles
AI总结：
提出SI-EDTL两阶段集成深度迁移学习模型，结合多种预训练特征提取器与分类器，经鲸鱼优化调参，在AU-AIR数据集上实现优于现有方法的无人机车辆检测。
摘要：
本文介绍了SI-EDTL，一种用于检测无人机图像中多辆车辆的两阶段群体智能集成深度迁移学习模型。它结合了三个预训练的Faster R-CNN特征提取器模型（InceptionV3、ResNet50、GoogLeNet）与五个迁移分类器（KNN、SVM、MLP、C4.5、朴素贝叶斯），从而产生15个不同的基学习器。这些基学习器通过加权平均进行聚合，以将区域分类为汽车、货车、卡车、公共汽车或背景。使用鲸鱼优化算法对超参数进行优化，以平衡准确率、精确率和召回率。在MATLAB R2020b中实现并采用并行处理，SI-EDTL在AU-AIR无人机数据集上优于现有方法。
英文摘要：
This paper introduces SI-EDTL, a two-stage swarm intelligence ensemble deep transfer learning model for detecting multiple vehicles in UAV images. It combines three pre-trained Faster R-CNN feature extractor models (InceptionV3, ResNet50, GoogLeNet) with five transfer classifiers (KNN, SVM, MLP, C4.5, Naïve Bayes), resulting in 15 different base learners. These are aggregated via weighted averaging to classify regions as Car, Van, Truck, Bus, or background. Hyperparameters are optimized with the whale optimization algorithm to balance accuracy, precision, and recall. Implemented in MATLAB R2020b with parallel processing, SI-EDTL outperforms existing methods on the AU-AIR UAV dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
