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YOLO-Arxiv-Daily

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每日从arXiv中获取最新YOLO相关论文

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# YOLO ArXiv Daily

[![Daily Papers](https://img.shields.io/badge/📅-每日更新-blue)]()
[![arXiv](https://img.shields.io/badge/arXiv-最新论文-red)](https://arxiv.org/)
[![Python](https://img.shields.io/badge/Python-3.10+-green)](https://www.python.org/)

*自动追踪 YOLO 相关最新论文，提供中英文双语摘要*

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## 📑 论文列表

> ### 1. The Role of Natural Language Understanding in Multimodal Video\-Based Dengue Diagnosis
>
> | 属性 | 内容 |
> |:---:|:---|
> | 📅 发布日期 | 2026-08-13 |
> | 👤 作者 | Danial Sharifrazi |
>
> **📄 英文摘要：**
> Detecting infection\-related behavioral changes in mosquitoes from video data is challenging because mosquitoes are small, move rapidly and irregularly, and are affected by environmental factors such as background, lighting, and shadows, which can make reliable feature extraction difficult. In this study, a YOLO\- and Contrastive Language\-Image Pre\-training \(CLIP\)\-based vision\-language framework is proposed to classify mosquito flight frames of uninfected and Dengue virus serotype 2 \(DENV2\)\-infected mosquitoes. First, YOLO is used to isolate mosquito regions from the background. Then, visual features extracted from video frames are aligned with biologically meaningful textual prompts in a shared embedding space. The multimodal model was fine\-tuned using supervised bidirectional contrastive learning and evaluated through frame\-level image\-text similarity\-based classification. The results show that the proposed method achieved 98.54% accuracy and 99.91% sensitivity at the frame level. After temporal aggregation of frame\-level information, the model achieved complete video\-level performance. The ablation results showed that fine\-tuning and CLIP\-based representations were essential for this domain, while the textual branch provided semantic image\-text alignment rather than an accuracy advantage over the vision\-only model. These findings suggest that vision\-language models can provide a useful framework for analyzing infection\-related biological behaviors from video data.
>
> 🔗 [阅读论文](http://arxiv.org/abs/2608.12677v1)

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> ### 2. Do Not Forget the Obvious \- RISC: A Risk\-Informed Slice\-Coverage Protocol for Safe Autonomous Driving
>
> | 属性 | 内容 |
> |:---:|:---|
> | 📅 发布日期 | 2026-08-12 |
> | 👤 作者 | Fabian Hüger |
>
> **📄 英文摘要：**
> Aggregate metrics may not fully reflect performance in insufficiently examined high\-risk driving conditions. We propose RISC \(Risk\-Informed Slice Coverage\), a practical protocol for risk\-guided stress testing and coverage\-qualified evaluation. Risk\-guided stress testing directs a finite audit budget toward risk\-relevant sub\-datasets, called risk slices, while coverage\-qualified evaluation reports results together with explicit statements about which slices are sufficiently or insufficiently covered. The protocol translates safety concerns into machine\-readable risk slices, uses lightweight signals to tag candidate data, selects a compact audit set by risk, and qualifies the results using coverage evidence. An LLM can optionally support this process by surfacing relevant but potentially overlooked conditions during test planning, thereby helping engineers not to forget the obvious. RISC is model\-agnostic and can be applied to perception modules, driving models, and other autonomous\-driving subsystems. We instantiate the protocol for monocular pedestrian perception using 1,000 frames from the Zenseact Open Dataset, image statistics, and a YOLO\-based detector proxy. In this proof\-of\-concept study, risk\-guided selection increases critical failure discovery from 34.0% under random sampling to 98.5%. RISC provides a lightweight, assurance\-oriented evaluation layer that complements scenario categorization, coverage assessment, and broader testing\-and\-verification workflows.
>
> 🔗 [阅读论文](http://arxiv.org/abs/2608.12051v1)

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> ### 3. A Hybrid Framework of Vision Transformer and Gated Recurrent Unit for Detection of Mosquito Diseases
>
> | 属性 | 内容 |
> |:---:|:---|
> | 📅 发布日期 | 2026-08-12 |
> | 👤 作者 | Danial Sharifrazi |
>
> **📄 英文摘要：**
> Identifying dengue virus\-infected mosquitoes from control mosquitoes is a major challenge in analyzing mosquito locomotion behavior due to the small size and complexity of the video background. Conventional AI methods are often unable to extract accurate features from video frames and produce erroneous features. In this study, a three\-step framework is introduced: first, mosquitoes are identified and the background is removed using the YOLO 11M model, then visual features are extracted using the Vision Transformer \(ViT\), and finally the videos are classified with a convolutional GRU \(ConvGRU\) classifier. A comparative analysis of different models, including Recurrent Neural Network \(RNN\), Long Short\-Term Memory \(LSTM\), Gated Recurrent Unit \(GRU\), and their convolutional versions showed that the ConvGRU model achieved the best performance; it achieved 88.88% accuracy, 84.45% precision, 82.82% recall, and 82.81% F1 score. These results demonstrate that combining convolutional models with sequence\-based networks, especially in the ConvGRU model, allows the simultaneous extraction of precise spatial features and long\-term temporal dependencies from mosquito movements. Finally, the proposed framework provides a reliable solution for analyzing mosquito behavior in complex environments.
>
> 🔗 [阅读论文](http://arxiv.org/abs/2608.11582v1)

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> ### 4. A Comparative Evaluation of Deep Learning Object Detection Models on a Real\-World Multi\-Plant Dataset from Africa
>
> | 属性 | 内容 |
> |:---:|:---|
> | 📅 发布日期 | 2026-08-11 |
> | 👤 作者 | Ismail Ismail Tijjani |
>
> **📄 英文摘要：**
> The application of computer vision in agriculture has shown significant potential for improving crop monitoring and precision farming. However, many existing approaches rely on controlled datasets that do not adequately represent realworld farming conditions, particularly in underrepresented regions such as Africa. This study presents a comparative evaluation of six object detection models YOLOv5, YOLOv8, YOLO11, YOLO26, Faster R\-CNN, and RT\-DETR using a real\-world dataset, AgriAISeg 1 , collected manually from Nigerian farms. AgriAISeg comprises 3,382 images of sesame, cabbage, and tomato crops captured under varying environmental conditions, including changes in illumination, occlusion, and viewing perspectives. Models were trained, and performance was assessed using precision, recall, mAP@0.5, and mAP@0.5:0.95. The results show that RT\-DETR achieved the highest overall performance with a precision of 0.768 and mAP@0.5:0.95 of 0.624, while YOLOv8 and YOLO11 also demonstrated strong and consistent performance. In contrast, Faster R\-CNN recorded significantly lower accuracy, with an overall mAP@0.5 of 0.466, indicating reduced effectiveness under complex field conditions. In addition, YOLO\-based models exhibited superior training efficiency compared to Faster R\-CNN.These findings demonstrate that modern one\-stage and transformer\-based detectors provide more reliable and efficient solutions for plant detection in realworld agricultural environments.
>
> 🔗 [阅读论文](http://arxiv.org/abs/2608.11053v1)

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> ### 5. MammoMix: Leveraging Mixture of Experts for Robust Mammogram Breast Detection
>
> | 属性 | 内容 |
> |:---:|:---|
> | 📅 发布日期 | 2026-08-11 |
> | 👤 作者 | Dinh Tan Nguyen |
>
> **📄 英文摘要：**
> Breast lesion detection in mammography remains a challenging task due to variations in image quality, lesion appearance, and population demographics across datasets. While current object detectors such as YOLO and DETR achieve strong results on individual datasets, their performance often degrades when trained on or applied across heterogeneous sources. To address this, we propose MammoMix, a novel framework based on Mixture\-of\-Experts \(MoE\) paradigm for robust and generalizable lesion detection. In MammoMix, each expert model is trained on a specific domain, allowing it to specialize in distinct characteristics of its source data. A gating mechanism adaptively weighs contributions from each expert based on input image, combining their outputs to enable domain\-adaptive inference. To improve reliability, we further incorporate a calibration module, MoCAE, which adjusts confidence scores to reflect true predictive uncertainty. We evaluate MammoMix on 3 public mammography datasets: CSAW, DDSM, and DMID, covering diverse clinical settings. Results show that MammoMix outperforms baseline detectors in both average precision and reliability, particularly on datasets with greater variability. Our findings demonstrate that expert specialization and calibrated ensemble fusion significantly enhance model generalization and robustness. MammoMix offers a promising step toward dependable AI\-assisted breast cancer screening across real\-world clinical domains.
>
> 🔗 [阅读论文](http://arxiv.org/abs/2608.10437v1)

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