arXiv Computer Vision September 11, 2026 By Rakesh Ranjan, Gajanan S. Kothawade, Kata Sharrer, Scott Tsukuda, Christopher Good

Does YOLO26 Truly Offer Advantages Over Its Predecessors for Edge Deployment? A Benchmark Study in Aquaculture

Read the original on arXiv Computer Vision →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

arXiv Machine Learning

Jul 15

A Comprehensive Evaluation of Deep Learning Object Detection Models on Heterogeneous Edge Devices

arXiv:2409. 16808v3 Announce Type: replace-cross Abstract: Modern applications such as autonomous vehicles, intelligent surveillance, and smart city systems increasingly require object detection on resource-constrained edge devices.

By Daghash K. Alqahtani, Muhammad Aamir Cheema, Maria A. Rodriguez, Adel N. Toosi

agents computer-vision benchmarks

arXiv AI

2d ago

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.

By Ranjan Sapkota, Manoj Karkee

llms computer-vision robotics efficiency benchmarks safety

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.

By Glenn Jocher, Jing Qiu, Mengyu Liu, Shuai Lyu, Fatih Cagatay Akyon, Muhammet Esat Kalfaoglu

llms computer-vision

arXiv Computer Vision

4d ago

Towards Practical Precision Agriculture: Real-Time Fruit Detection and Video Analytics on Embedded Edge Hardware

arXiv:2609.13551v1 Announce Type: new Abstract: Static-image benchmarks do not capture the computational and temporal requirements of practical orchard video analytics. This study presents an end-to-...

By Ivica Dimitrovski, Vlatko Spasev, Ivan Kitanovski, Petre Lameski, Dane Boshev

benchmarks

arXiv Machine Learning

4d ago

What Input Resolution Is Required for Bird Species Identification, and What Is Its Latency Cost on an Edge Device? A Study of 14 Input Resolutions and Six Architectures with On-Device Measurements

arXiv:2609.14247v1 Announce Type: cross Abstract: Bird-strike mitigation at wind farms requires identifying distant birds that span only tens of pixels, so the classifier's input resolution N is a de...

By Takeshi Nishikawa

llms

arXiv Computer Vision

Aug 25

Spiking Neural Networks for Energy-Efficient Object Detection in Forward-Looking Sonar Imagery

arXiv:2608.22072v1 Announce Type: new Abstract: Autonomous underwater vehicles (AUVs) are increasingly important tools in industries ranging from research, to energy, to defense. AUVs are power-const...

By Gwenevere Frank, Gert Cauwenberghs