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

Jul 16, 2026

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The You Only Look Once (YOLO) family is widely used in aquaculture monitoring because of real-time detection and flexible deployment. YOLO26 introduces end-to-end inference without Non-Maximum Suppression and targets deployment on resource-constrained CPU-based devices, yet its aquaculture-specific performance and efficiency versus prior YOLO generations were not validated. A benchmark compares YOLO26 with Ultralytics predecessors YOLOv5u, YOLOv8, and YOLO11 across nano to medium scales for fish-mortality detection in recirculating aquaculture systems, evaluating accuracy, training efficiency, and inference speed on NVIDIA A100 and Raspberry Pi 5 edge hardware.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages23

Reading Time58 min

Introduction

Motivation and challenges in aquaculture monitoring

CNN-based computer vision and deployment constraints

Model selection criteria for real-time inference

Does YOLO26 Truly Offer Advantages Over Its Predecessors for Edge Deployment? A Benchmark Study in Aquaculture Rakesh Ranjan 1,*, Gajanan S. Kothawade 1, Kata Sharrer 1, Scott Tsukuda 1, Christopher Good 1 1The Conservation Fund Freshwater Institute, Shepherdstown, WV, 25443 *Corresponding author, Email: [rranjan@conservationfund.org](rranjan@conservationfund.org), Phone: +1 (304) 870-2203 Abstract The You Only Look Once (YOLO) has been widely adopted in aquaculture monitoring and management due to its real-time performance and deployment flexibility. The recently introduced YOLO26 architecture incorporates Non-Maximum Suppression (NMS)-free end-to-end inference and is optimized for deployment on resource-constrained CPU-based devices, making it particularly relevant for edge deployment in commercial aquaculture applications. Nevertheless, its performance, operational efficiency, and deployment suitability compared with previous YOLO generations remain largely unvalidated in aquaculture-specific scenarios. This study benchmarks YOLO26 against three Ultralytics predecessors (YOLOv5u, YOLOv8, and YOLO11) across nano, small, and medium model scales for the detection of fish mortality, a critical indicator of fish population health and welfare, in recirculating aquaculture systems (RAS) . Twelve model variants were evaluated for detection accuracy, training efficiency across seven dataset sizes, and inference performance on both high-performance NVIDIA A100 GPUs and the resourceconstrained, CPU-only Raspberry Pi 5 edge device. All models achieved comparable performance on the full dataset, with mAP50 varying by only 1.04 percentage points, indicating minimal influence of architectural generation on final mortality detection accuracy when sufficient training data are available. However, notable differences emerged in data efficiency and deployment performance. YOLOv8 demonstrated the strongest training efficiency, achieving 90% mAP50 with only 400 training images, whereas YOLO26 nano and small variants required 1,000 images to reach comparable accuracy. In contrast, YOLO26 exhibited advantages during edge deployment, with YOLO26n achieving the highest inference speed on the Raspberry Pi 5 at 7.51 FPS, while YOLOv5mu outperformed all contemporary medium-scale architectures on CPU-based hardware. These results demonstrate that architectural novelty alone is an insufficient criterion for model selection. The findings support a deployment-oriented framework in which training data availability, target hardware, and inference requirements collectively inform model selection for aquaculture applications. Keywords: YOLO; fish mortality detection; edge deployment; precision aquaculture; recirculating aquaculture systems 1. Introduction With the growing global population and rising per capita seafood consumption, demand for aquaculture production continues to increase [1] . Intensive aquaculture systems, such as net pens and recirculating aquaculture systems (RAS), have shown promise to meet this growing demand by supplying affordable, high‑quality sea food [2,3] . However, intensive systems also introduce significant challenges related to sustainability, fish health and welfare, and environmental impacts [4,5] . For RAS, the high capital and operational costs of these systems place strong pressure on profit margins and demand a high resource efficiency to ensure economic viability [6] . Recent advances in precision aquaculture technology, particularly Computer Vision and Artificial Intelligence (AI)-aided automated monitoring and decisionsupport systems, offer a promising tool for mitigating operational challenges and maximizing yield in intensive production environments [7,8] . These methods enable automated, non-invasive, and continuous monitoring of key biological and operational parameters in aquaculture systems that were previously accessible only through costly, labor-intensive manual observations. Several studies

FAQs

What problem does the study address about YOLO26 in aquaculture?

The study benchmarks whether YOLO26 offers measurable performance, efficiency, and deployment suitability advantages over earlier YOLO generations specifically for aquaculture fish-mortality detection.

Which models and hardware platforms are compared in the benchmark?

YOLO26 is compared with Ultralytics predecessors YOLOv5u, YOLOv8, and YOLO11 across nano, small, and medium scales, with inference tested on NVIDIA A100 GPUs and the CPU-only Raspberry Pi 5 edge device.

What key findings relate accuracy to architectural changes?

Across the full dataset, models show comparable accuracy, with mAP50 varying by only 1.04 percentage points, suggesting architecture generation has minimal impact on final mortality detection accuracy when training data are sufficient.

Jul 16, 2026

Upload by Maeve