Abstract
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.
Abstract (translated)
这篇论文介绍了SI-EDTL，这是一种两阶段的群智能集成深度迁移学习模型，用于检测无人机图像中的多个车辆。该模型结合了三个预训练的Faster R-CNN特征提取器模型（InceptionV3、ResNet50和GoogLeNet）与五种迁移分类器（KNN、SVM、MLP、C4.5和朴素贝叶斯），共生成15个不同的基础学习器。这些基础学习器通过加权平均聚合，以将区域分类为小汽车、面包车、卡车、公共汽车或背景。该模型使用鲸鱼优化算法来调整超参数，从而在准确率、精确度和召回率之间取得平衡。SI-EDTL在MATLAB R2020b中实现，并采用并行处理，在AU-AIR无人机数据集上优于现有方法。
URL
https://arxiv.org/abs/2509.08026
