arXiv AI
Jun 3
Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models
arXiv:2606. 03748v1 Announce Type: cross Abstract: Real-time vision demands models that are accurate, efficient, and simple to deploy across diverse hardware.
Ultralytics YOLO Evolution: An Overview of YOLO27, YOLO26, YOLO11, YOLOv8, and YOLOv5 Object Detectors for Computer Vision and Pattern Recognition
This paper provides a detailed overview of the Ultralytics YOLO family from YOLOv5 to YOLO27, highlighting key architectural changes, benchmarking results, and deployment considerations. It discusses the evolution of each version—YOLO27’s scale‑adaptive dual architecture, YOLO26’s loss and optimization refinements, YOLO11’s efficiency focus, YOLOv8’s anchor‑free detection, and YOLOv5’s modular ecosystem—alongside performance metrics on COCO and latency on TensorRT. The review also surveys applications in robotics, agriculture, surveillance, and manufacturing, and outlines future challenges such as dense scene handling, CNN‑Transformer integration, and hardware‑aware optimization.
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arXiv Computer Vision
Sep 22
HDMamba-YOLO: Efficient State-Space Perception and Local Spatial Reconstruction for UAV Small Object
arXiv:2609.23061v1 Announce Type: new Abstract: Small-object detection in UAV imagery is challenged by weak visual evidence, ambiguous boundaries, dense object distributions, and complex backgrounds....
arXiv AI
Jun 24
From Spatial to Spectral: An Efficient, Frequency-Guided Feature Representation Learner for Small Object Detection
arXiv:2606. 23825v1 Announce Type: cross Abstract: Efficient small object detection is bottlenecked by the inherent feature scarcity of tiny targets, which is further aggravated by operations of spatial-domain detectors that indiscriminately discard critical high-frequency details.
arXiv Computer Vision
Oct 23
EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation
EdgeCrafter presents a compact Vision Transformer framework tailored for edge dense prediction tasks such as object detection, instance segmentation, and human pose estimation. By employing task-specialized distillation and an edge-friendly encoder-decoder design, the ECDet backbone achieves 51.7 box AP with under 10 M parameters, while its extensions reach 48.4 mask AP and 74.8 keypoint AP using only COCO annotations. Pretraining on Objects365 further boosts performance, demonstrating that lightweight ViTs can compete with CNN-based models on resource-constrained devices.
arXiv Computer Vision
Sep 15
YOLO12-MambaScan: An Efficient Object Detector with High-Frequency Enhancement and State-Space Modeling
arXiv:2609.13647v1 Announce Type: new Abstract: The rapid development of unmanned aerial vehicle (UAV) technology has made aerial-image object detection increasingly important for natural-resource mo...
arXiv AI
Jul 9
MiLSD: A Micro Line-Segment Detector for Resource-Constrained Devices
arXiv:2607. 06600v1 Announce Type: cross Abstract: Line segment detection is a key building block in visual SLAM, 3D reconstruction, and industrial inspection.
arXiv Computer Vision
Sep 21
Optimizing YOLO27, YOLO26, YOLO11, and YOLOv8 for Fine-Grained Small-Object Detection and Segmentation in Complex Orchard Environments
The paper compares Ultralytics YOLO27, YOLO26, YOLO11, and YOLOv8 for detecting and segmenting small fruit parts in orchard settings. It evaluates five model scales across 30 experiments, finding that YOLO11s-960 and YOLO26s-960 achieve the best mask and box mAP scores while maintaining efficient parameter counts. The study also highlights the difficulty of peduncle detection and provides publicly available code and models for reproducibility.
arXiv AI
Sep 10
TriCCOT: Tri-part Convolutional Conformal Transformer for Onboard Space Object Detection
TriCCOT is a tri-part architecture designed for onboard space object detection that balances computational efficiency with robust performance. It combines a convolutional region proposal network, a conformal prediction stage that enlarges bounding boxes with distribution‑free probabilistic coverage, and Aper‑GATES—a hardware‑friendly attention‑based classifier that replaces standard transformer operations with convolutional projections and gating. Experiments on DIOR and VDVRaw datasets show competitive detection accuracy and improved robustness to blur and noise, and the model was fully deployed on a Xilinx Versal VCK190 FPGA without altering the underlying DPU architecture.
arXiv Computer Vision
Aug 21
YOLOv14: Adaptive Real-Time Object Detection for Diverse Imaging Conditions
arXiv:2608. 04720v4 Announce Type: replace Abstract: Real-time object detectors achieve remarkable accuracy under controlled conditions, yet degrade sharply on non-ideal inputs-fisheye distortion, game-rendered content, aerial views, and 360{\deg}panoramas.
arXiv Machine Learning
Sep 23
GTR: Gated Token Recurrence for Efficient Dense Prediction
The paper introduces Gated Token Recurrence (GTR), a softmax‑free recurrent vision backbone that replaces global softmax attention with gated linear attention, alternating scan directions, and enhanced SwiGLU blocks. GTR is distilled from a DINOv3 teacher using only final‑layer patch‑token alignment, and achieves strong performance on COCO object detection (58.9 box AP) with very low latency (1.908 ms on an RTX 4090). The backbone also transfers to multiple dense prediction tasks and runs efficiently on edge hardware via a specialized CUDA operator and TensorRT deployment.
arXiv Computer Vision
Sep 11
Does YOLO26 Truly Offer Advantages Over Its Predecessors for Edge Deployment? A Benchmark Study in Aquaculture
The paper evaluates the new YOLO26 architecture, which offers NMS-free end-to-end inference and is tailored for CPU-based edge devices, against three earlier Ultralytics models (YOLOv5u, YOLOv8, and YOLO11) in aquaculture fish mortality detection. Across nano, small, and medium scales, all models achieved similar detection accuracy on a full dataset, but differences emerged in data efficiency and deployment performance: YOLOv8 reached 90% mAP50 with only 400 images, while YOLO26 variants needed 1,000 images; YOLO26n was fastest on a Raspberry Pi 5 (7.51 FPS), whereas YOLOv5mu led on CPU-based hardware. The study concludes that architectural novelty alone does not dictate suitability for edge AI in aquaculture; training data size, target hardware, and inference needs must be jointly considered.
arXiv Computer Vision
Oct 12
JMT-YOLO: Multi-task Domain Adaptation via Segmentation-Guided Consistency Learning
JMT-YOLO is a new joint multi-task learning framework for domain adaptive object detection that builds on the Mean Teacher architecture. It adds an auxiliary semantic segmentation task and a masking mechanism to better model foreground–background boundaries and improve instance-level alignment. Experiments on Pascal VOC→Clipart1k, Cityscapes→Foggy Cityscapes, and KITTI→Cityscapes show that JMT‑YOLO outperforms existing state‑of‑the‑art methods, achieving 46.9%, 57.3%, and 61.0% detection performance respectively.