Cost-Effective Class-Imbalance Aware CNN for Vehicle Localization and Categorization in High Resolution Aerial Images

SF-DETR: A Scale-Frequency Detection Transformer for Drone-View Object Detection

Drone-based object detection faces critical challenges, including tiny objects, complex urban backgrounds, dramatic scale variations, and high-frequency detail loss during feature propagation. Current detection methods struggle to address these challenges while maintaining computational efficiency effectively. We propose Scale-Frequency Detection Transformer (SF-DETR), a novel end-to-end framework for drone-view scenarios. SF-DETR introduces a lightweight ScaleFormerNet backbone with Dual Scale Vision Transformer modules, a Bilateral Interactive Feature Enhancement Module, and a Multi-Scale Frequency-Fused Feature Enhancement Network. Extensive experiments on the VisDrone2019 dataset demonstrate SF-DETR's superior performance, achieving 51.0% mAP50 and 31.8% mAP50:95, surpassing state-of-the-art methods like YOLOv9m and RTDETR-r18 by 6.2% and 4.0%, respectively. Further validation of the HIT-UAV dataset confirms the model's generalization capability. Our work establishes a new benchmark for drone-view object detection and provides lightweight architecture suitable for embedded device deployment in real-world aerial surveillance applications.

An Ensemble Broad Learning Scheme for Semisupervised Vehicle Type Classification

Nowadays vehicle type classification is a fundamental part of intelligent transportation systems (ITSs) and is widely used in various applications like traffic flow monitoring, security enforcement, and autonomous driving, etc. However, vehicle classification is usually used in supervised learning, which greatly limits the applicability for real ITS. This article proposes a semisupervised vehicle type classification scheme via ensemble broad learning for ITS. This presented method contains two main parts. In the first part, a collection of base broad learning system (BLS) classifiers is trained by semisupervised learning to avoid time-consuming training process and alleviate the increasingly unlabeled samples burden. In the second part, a dynamic ensemble structure constructed by trained classifier groups with different characteristics obtains the highest type probability and determine which the vehicle belongs, so as to achieve superior generalization performance than a single base classifier. Several experiments conducted on the pubic BIT-Vehicle dataset and MIO-TCD dataset demonstrate that the proposed method outperforms single BLS classifier and some mainstream methods on effectiveness and efficiency.

An Impartial Semi-Supervised Learning Strategy for Imbalanced Classification on VHR Images

Imbalanced learning is a common problem in remote sensing imagery-based land-use and land-cover classifications. Imbalanced learning can lead to a reduction in classification accuracy and even the omission of the minority class. In this paper, an impartial semi-supervised learning strategy based on extreme gradient boosting (ISS-XGB) is proposed to classify very high resolution (VHR) images with imbalanced data. ISS-XGB solves multi-class classification by using several semi-supervised classifiers. It first employs multi-group unlabeled data to eliminate the imbalance of training samples and then utilizes gradient boosting-based regression to simulate the target classes with positive and unlabeled samples. In this study, experiments were conducted on eight study areas with different imbalanced situations. The results showed that ISS-XGB provided a comparable but more stable performance than most commonly used classification approaches (i.e., random forest (RF), XGB, multilayer perceptron (MLP), and support vector machine (SVM)), positive and unlabeled learning (PU-Learning) methods (PU-BP and PU-SVM), and typical synthetic sample-based imbalanced learning methods. Especially under extremely imbalanced situations, ISS-XGB can provide high accuracy for the minority class without losing overall performance (the average overall accuracy achieves 85.92%). The proposed strategy has great potential in solving the imbalanced classification problems in remote sensing.

A Review on Vehicle Classification and Potential Use of Smart Vehicle-Assisted Techniques

Vehicle classification (VC) is an underlying approach in an intelligent transportation system and is widely used in various applications like the monitoring of traffic flow, automated parking systems, and security enforcement. The existing VC methods generally have a local nature and can classify the vehicles if the target vehicle passes through fixed sensors, passes through the short-range coverage monitoring area, or a hybrid of these methods. Using global positioning system (GPS) can provide reliable global information regarding kinematic characteristics; however, the methods lack information about the physical parameter of vehicles. Furthermore, in the available studies, smartphone or portable GPS apparatuses are used as the source of the extraction vehicle's kinematic characteristics, which are not dependable for the tracking and classification of vehicles in real time. To deal with the limitation of the available VC methods, potential global methods to identify physical and kinematic characteristics in real time states are investigated. Vehicular Ad Hoc Networks (VANETs) are networks of intelligent interconnected vehicles that can provide traffic parameters such as type, velocity, direction, and position of each vehicle in a real time manner. In this study, VANETs are introduced for VC and their capabilities, which can be used for the above purpose, are presented from the available literature. To the best of the authors' knowledge, this is the first study that introduces VANETs for VC purposes. Finally, a comparison is conducted that shows that VANETs outperform the conventional techniques.