Tootfinder

Ultralytics, which develops the YOLO series of open-source computer vision models and sells commercial licenses, raised a $30M Series A led by Elephant VC (Maria Deutscher/SiliconANGLE)
https://siliconangle.com/2025/09/17/computer-vision-startup-ul…

Computer vision startup Ultralytics raises $30M in funding - SiliconANGLE
Computer vision startup Ultralytics raises $30M in funding - SiliconANGLE

TACR-YOLO: A Real-time Detection Framework for Abnormal Human Behaviors Enhanced with Coordinate and Task-Aware Representations
Xinyi Yin, Wenbo Yuan, Xuecheng Wu, Liangyu Fu, Danlei Huang
https://arxiv.org/abs/2508.11478

TACR-YOLO: A Real-time Detection Framework for Abnormal Human Behaviors Enhanced with Coordinate and Task-Aware Representations
Abnormal Human Behavior Detection (AHBD) under special scenarios is becoming increasingly crucial. While YOLO-based detection methods excel in real-time tasks, they remain hindered by challenges including small objects, task conflicts, and multi-scale fusion in AHBD. To tackle them, we propose TACR-YOLO, a new real-time framework for AHBD. We introduce a Coordinate Attention Module to enhance small object detection, a Task-Aware Attention Module to deal with classification-regression conflicts,…

A Novel Attention-Augmented Wavelet YOLO System for Real-time Brain Vessel Segmentation on Transcranial Color-coded Doppler
Wenxuan Zhang (, Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China), Shuai Li (, Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China), Xinyi Wang (, Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China), Yu Sun (, Department of Bi…

A Novel Attention-Augmented Wavelet YOLO System for Real-time Brain Vessel Segmentation on Transcranial Color-coded Doppler
The Circle of Willis (CoW), vital for ensuring consistent blood flow to the brain, is closely linked to ischemic stroke. Accurate assessment of the CoW is important for identifying individuals at risk and guiding appropriate clinical management. Among existing imaging methods, Transcranial Color-coded Doppler (TCCD) offers unique advantages due to its radiation-free nature, affordability, and accessibility. However, reliable TCCD assessments depend heavily on operator expertise for identifying …

YOLO-CIANNA: Galaxy detection with deep learning in radio data: II. Winning the SKA SDC2 using a generalized 3D-YOLO network
D. Cornu, B. Semelin, P. Salom\'e, X. Lu, S. Aicardi, J. Freundlich, F. Mertens, A. Marchal, G. Sainton, F. Combes, C. Tasse
https://arxiv.org/abs/2509.12082

YOLO-CIANNA: Galaxy detection with deep learning in radio data: II. Winning the SKA SDC2 using a generalized 3D-YOLO network
As the scientific exploitation of the Square Kilometre Array (SKA) approaches, there is a need for new advanced data analysis and visualization tools capable of processing large high-dimensional datasets. In this study, we aim to generalize the YOLO-CIANNA deep learning source detection and characterization method for 3D hyperspectral HI emission cubes. We present the adaptations we made to the regression-based detection formalism and the construction of an end-to-end 3D convolutional neural ne…

Performance Optimization of YOLO-FEDER FusionNet for Robust Drone Detection in Visually Complex Environments
Tamara R. Lenhard, Andreas Weinmann, Tobias Koch
https://arxiv.org/abs/2509.14012

Performance Optimization of YOLO-FEDER FusionNet for Robust Drone Detection in Visually Complex Environments
Drone detection in visually complex environments remains challenging due to background clutter, small object scale, and camouflage effects. While generic object detectors like YOLO exhibit strong performance in low-texture scenes, their effectiveness degrades in cluttered environments with low object-background separability. To address these limitations, this work presents an enhanced iteration of YOLO-FEDER FusionNet -- a detection framework that integrates generic object detection with camouf…

Suddenly, a kitty cat belly appears. It beckons to you... Will you risk it?

Mechanical Automation with Vision: A Design for Rubik's Cube Solver
Abhinav Chalise, Nimesh Gopal Pradhan, Nishan Khanal, Prashant Raj Bista, Dinesh Baniya Kshatri
https://arxiv.org/abs/2508.12469 …

Mechanical Automation with Vision: A Design for Rubik's Cube Solver
The core mechanical system is built around three stepper motors for physical manipulation, a microcontroller for hardware control, a camera and YOLO detection model for real-time cube state detection. A significant software component is the development of a user-friendly graphical user interface (GUI) designed in Unity. The initial state after detection from real-time YOLOv8 model (Precision 0.98443, Recall 0.98419, Box Loss 0.42051, Class Loss 0.2611) is virtualized on GUI. To get the solution…

Updated everything yolo

Why’d they call it “agentic AI” when Russian roulette was right there?
(Ah, because Russian roulette doesn’t destroy our habitat, drink all our water, and doom our species. I get it now, my bad.) https://front-end.social/@ariel/114675226611700916

Ariel Salminen (@ariel@front-end.social)
Attached: 1 image “Cursor […] tried to delete some old files, didn’t work at the first time and it decided to end up deleting everything on my computer, including itself.” 😶‍🌫️ https://forum.cursor.com/t/cursor-yolo-deleted-everything-in-my-computer/103131

YOLO-based Bearing Fault Diagnosis With Continuous Wavelet Transform
Po-Heng Chou, Wei-Lung Mao, Ru-Ping Lin
https://arxiv.org/abs/2509.03070 https://arxiv…

YOLO-based Bearing Fault Diagnosis With Continuous Wavelet Transform
This letter proposes a YOLO-based framework for spatial bearing fault diagnosis using time-frequency spectrograms derived from continuous wavelet transform (CWT). One-dimensional vibration signals are first transformed into time-frequency spectrograms using Morlet wavelets to capture transient fault signatures. These spectrograms are then processed by YOLOv9, v10, and v11 models to classify fault types. Evaluated on three benchmark datasets, including Case Western Reserve University (CWRU), Pad…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[3/5]:
- A Co-Training Semi-Supervised Framework Using Faster R-CNN and YOLO Networks for Object Detection...
Hossein Yazdanjouei, Arash Mansouri, Mohammad Shokouhifar

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/4]:
- SBP-YOLO:A Lightweight Real-Time Model for Detecting Speed Bumps and Potholes
Chuanqi Liang, Jie Fu, Miao Yu, Lei Luo

Today I finally remembered the syntax to delete a remote git branch: `git push origin --delete 42_not_needed_branch` (and was brave enought to just execute it without checking, yolo)

A Single Detect Focused YOLO Framework for Robust Mitotic Figure Detection
Yasemin Topuz, M. Taha G\"okcan, Serdar Y{\i}ld{\i}z, Song\"ul Varl{\i}
https://arxiv.org/abs/2509.02637

A Single Detect Focused YOLO Framework for Robust Mitotic Figure Detection
Mitotic figure detection is a crucial task in computational pathology, as mitotic activity serves as a strong prognostic marker for tumor aggressiveness. However, domain variability that arises from differences in scanners, tissue types, and staining protocols poses a major challenge to the robustness of automated detection methods. In this study, we introduce SDF-YOLO (Single Detect Focused YOLO), a lightweight yet domain-robust detection framework designed specifically for small, rare targets…

Utilizing Vision-Language Models as Action Models for Intent Recognition and Assistance
Cesar Alan Contreras, Manolis Chiou, Alireza Rastegarpanah, Michal Szulik, Rustam Stolkin
https://arxiv.org/abs/2508.11093

Utilizing Vision-Language Models as Action Models for Intent Recognition and Assistance
Human-robot collaboration requires robots to quickly infer user intent, provide transparent reasoning, and assist users in achieving their goals. Our recent work introduced GUIDER, our framework for inferring navigation and manipulation intents. We propose augmenting GUIDER with a vision-language model (VLM) and a text-only language model (LLM) to form a semantic prior that filters objects and locations based on the mission prompt. A vision pipeline (YOLO for object detection and the Segment An…

Don't have anyone to walk the Airborne with this year.
Got half a mind to walk solo and hang a sign around my neck "Free hugs and/or headpats".
Might just yolo it.

YOLO-Count: Differentiable Object Counting for Text-to-Image Generation
Guanning Zeng, Xiang Zhang, Zirui Wang, Haiyang Xu, Zeyuan Chen, Bingnan Li, Zhuowen Tu
https://arxiv.org/abs/2508.00728

YOLO-Count: Differentiable Object Counting for Text-to-Image Generation
We propose YOLO-Count, a differentiable open-vocabulary object counting model that tackles both general counting challenges and enables precise quantity control for text-to-image (T2I) generation. A core contribution is the 'cardinality' map, a novel regression target that accounts for variations in object size and spatial distribution. Leveraging representation alignment and a hybrid strong-weak supervision scheme, YOLO-Count bridges the gap between open-vocabulary counting and T2I generation …

GW-YOLO: Multi-transient segmentation in LIGO using computer vision
Siddharth Soni, Nikhil Mukund, Erik Katsavounidis
https://arxiv.org/abs/2508.17399 https://

GW-YOLO: Multi-transient segmentation in LIGO using computer vision
Time series data and their time-frequency representation from gravitational-wave interferometers present multiple opportunities for the use of artificial intelligence methods associated with signal and image processing. Closely connected with this is the real-time aspect associated with gravitational-wave interferometers and the astrophysical observations they perform; the discovery potential of these instruments can be significantly enhanced when data processing can be achieved in O(1s) timesc…

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment
Elena Camuffo, Francesco Barbato, Mete Ozay, Simone Milani, Umberto Michieli
https://arxiv.org/abs/2509.14001 https…

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment
We introduce MOCHA (Multi-modal Objects-aware Cross-arcHitecture Alignment), a knowledge distillation approach that transfers region-level multimodal semantics from a large vision-language teacher (e.g., LLaVa) into a lightweight vision-only object detector student (e.g., YOLO). A translation module maps student features into a joint space, where the training of the student and translator is guided by a dual-objective loss that enforces both local alignment and global relational consistency. Un…

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/4]:
- HOTSPOT-YOLO: A Lightweight Deep Learning Attention-Driven Model for Detecting Thermal Anomalies ...
Mahmoud Dhimish

“The A’s have clarified their position. And by ‘clarified their position,’ we mean ‘challenged the Colorado Rockies for the title of worst #baseball team on earth.’”

The Athletics Have Hopped Back Into The Dumpster | Defector
There was a time not that long ago when the West Sacramento Athletics were positioning themselves to replace The Hunny Club as the pride of Yolo County, which would have been a true festival of inner conflict for the region. By “not that long ago,” we mean May 5. Since then, the A’s have clarified their position. […]

YOLO Ensemble for UAV-based Multispectral Defect Detection in Wind Turbine Components
Serhii Svystun, Pavlo Radiuk, Oleksandr Melnychenko, Oleg Savenko, Anatoliy Sachenko
https://arxiv.org/abs/2509.04156

YOLO Ensemble for UAV-based Multispectral Defect Detection in Wind Turbine Components
Unmanned aerial vehicles (UAVs) equipped with advanced sensors have opened up new opportunities for monitoring wind power plants, including blades, towers, and other critical components. However, reliable defect detection requires high-resolution data and efficient methods to process multispectral imagery. In this research, we aim to enhance defect detection accuracy through the development of an ensemble of YOLO-based deep learning models that integrate both visible and thermal channels. We pr…

Ensemble YOLO Framework for Multi-Domain Mitotic Figure Detection in Histopathology Images
Navya Sri Kelam, Akash Parekh, Saikiran Bonthu, Nitin Singhal
https://arxiv.org/abs/2509.02957

Ensemble YOLO Framework for Multi-Domain Mitotic Figure Detection in Histopathology Images
Accurate detection of mitotic figures in whole slide histopathological images remains a challenging task due to their scarcity, morphological heterogeneity, and the variability introduced by tissue preparation and staining protocols. The MIDOG competition series provides standardized benchmarks for evaluating detection approaches across diverse domains, thus motivating the development of generalizable deep learning models. In this work, we investigate the performance of two modern one-stage det…

HOTSPOT-YOLO: A Lightweight Deep Learning Attention-Driven Model for Detecting Thermal Anomalies in Drone-Based Solar Photovoltaic Inspections
Mahmoud Dhimish
https://arxiv.org/abs/2508.18912

HOTSPOT-YOLO: A Lightweight Deep Learning Attention-Driven Model for Detecting Thermal Anomalies in Drone-Based Solar Photovoltaic Inspections
Thermal anomaly detection in solar photovoltaic (PV) systems is essential for ensuring operational efficiency and reducing maintenance costs. In this study, we developed and named HOTSPOT-YOLO, a lightweight artificial intelligence (AI) model that integrates an efficient convolutional neural network backbone and attention mechanisms to improve object detection. This model is specifically designed for drone-based thermal inspections of PV systems, addressing the unique challenges of detecting sm…

A Comparative Study of Controllability, Explainability, and Performance in Dysfluency Detection Models
Eric Zhang, Li Wei, Sarah Chen, Michael Wang
https://arxiv.org/abs/2509.00058

A Comparative Study of Controllability, Explainability, and Performance in Dysfluency Detection Models
Recent advances in dysfluency detection have introduced a variety of modeling paradigms, ranging from lightweight object-detection inspired networks (YOLOStutter) to modular interpretable frameworks (UDM). While performance on benchmark datasets continues to improve, clinical adoption requires more than accuracy: models must be controllable and explainable. In this paper, we present a systematic comparative analysis of four representative approaches--YOLO-Stutter, FluentNet, UDM, and SSDM--alon…

Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos
Yitong Quan, Benjamin Kiefer, Martin Messmer, Andreas Zell
https://arxiv.org/abs/2506.20550

Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos
Modern image-based object detection models, such as YOLOv7, primarily process individual frames independently, thus ignoring valuable temporal context naturally present in videos. Meanwhile, existing video-based detection methods often introduce complex temporal modules, significantly increasing model size and computational complexity. In practical applications such as surveillance and autonomous driving, transient challenges including motion blur, occlusions, and abrupt appearance changes can …

A New Hybrid Model of Generative Adversarial Network and You Only Look Once Algorithm for Automatic License-Plate Recognition
Behnoud Shafiezadeh, Amir Mashmool, Farshad Eshghi, Manoochehr Kelarestaghi
https://arxiv.org/abs/2509.06868

A New Hybrid Model of Generative Adversarial Network and You Only Look Once Algorithm for Automatic License-Plate Recognition
Automatic License-Plate Recognition (ALPR) plays a pivotal role in Intelligent Transportation Systems (ITS) as a fundamental element of Smart Cities. However, due to its high variability, ALPR faces challenging issues more efficiently addressed by deep learning techniques. In this paper, a selective Generative Adversarial Network (GAN) is proposed for deblurring in the preprocessing step, coupled with the state-of-the-art You-Only-Look-Once (YOLO)v5 object detection architectures for License-Pl…

Research on Improving the High Precision and Lightweight Diabetic Retinopathy Detection of YOLOv8n
Fei Yuhuan, Sun Xufei, Zang Ran, Wang Gengchen, Su Meng, Liu Fenghao
https://arxiv.org/abs/2507.00780

Research on Improving the High Precision and Lightweight Diabetic Retinopathy Detection of YOLOv8n
Early detection and diagnosis of diabetic retinopathy is one of the current research focuses in ophthalmology. However, due to the subtle features of micro-lesions and their susceptibility to background interference, ex-isting detection methods still face many challenges in terms of accuracy and robustness. To address these issues, a lightweight and high-precision detection model based on the improved YOLOv8n, named YOLO-KFG, is proposed. Firstly, a new dynamic convolution KWConv and C2f-KW mod…

An Investigation of Visual Foundation Models Robustness
Sandeep Gupta, Roberto Passerone
https://arxiv.org/abs/2508.16225 https://arxiv.org/pdf/2508.16225

An Investigation of Visual Foundation Models Robustness
Visual Foundation Models (VFMs) are becoming ubiquitous in computer vision, powering systems for diverse tasks such as object detection, image classification, segmentation, pose estimation, and motion tracking. VFMs are capitalizing on seminal innovations in deep learning models, such as LeNet-5, AlexNet, ResNet, VGGNet, InceptionNet, DenseNet, YOLO, and ViT, to deliver superior performance across a range of critical computer vision applications. These include security-sensitive domains like bi…

BetterCheck: Towards Safeguarding VLMs for Automotive Perception Systems
Malsha Ashani Mahawatta Dona, Beatriz Cabrero-Daniel, Yinan Yu, Christian Berger
https://arxiv.org/abs/2507.17722

BetterCheck: Towards Safeguarding VLMs for Automotive Perception Systems
Large language models (LLMs) are growingly extended to process multimodal data such as text and video simultaneously. Their remarkable performance in understanding what is shown in images is surpassing specialized neural networks (NNs) such as Yolo that is supporting only a well-formed but very limited vocabulary, ie., objects that they are able to detect. When being non-restricted, LLMs and in particular state-of-the-art vision language models (VLMs) show impressive performance to describe eve…