# YOLOv11 Optimization for Efficient Resource Utilization

arXiv: https://arxiv.org/abs/2412.14790 · HTML: https://mlsanity.com/paper/2412.14790/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 40 references that are in the map.
- First posted on arXiv 19 Dec 2024.

## Builds on (most cited first, 11 of 11)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Object Detection in 20 Years: A Survey](https://mlsanity.com/paper/1905.05055/) (2019)
- [YOLOv11: An Overview of the Key Architectural Enhancements](https://mlsanity.com/paper/2410.17725/) (2024)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [Scaled-YOLOv4: Scaling Cross Stage Partial Network](https://mlsanity.com/paper/2011.08036/) (2020)
- [The Computational Limits of Deep Learning](https://mlsanity.com/paper/2007.05558/) (2020)
- [Recent Advances in Deep Learning for Object Detection](https://mlsanity.com/paper/1908.03673/) (2019)
- [Roboflow 100: A Rich, Multi-Domain Object Detection Benchmark](https://mlsanity.com/paper/2211.13523/) (2022)
- [YOLOv11 for Vehicle Detection: Advancements, Performance, and Applications in Intelligent Transportation Systems](https://mlsanity.com/paper/2410.22898/) (2024)

## Built on by (most cited first, 4 of 4)

- [Enhancing YOLOv11n for Reliable Child Detection in Noisy Surveillance Footage](https://mlsanity.com/paper/2602.10592/) (2026)
- [Bearded Dragon Activity Recognition Pipeline: An AI-Based Approach to Behavioural Monitoring](https://mlsanity.com/paper/2507.17987/) (2025)
- [Automated multi-class wound assessment using dedicated instance segmentation models for boundary detection and classification](https://mlsanity.com/paper/2603.27325/) (2026)
- [Infectious Bovine Pinkeye Detection Using Computer Vision and Imbalance-Aware Learning](https://mlsanity.com/paper/2609.23714/) (2026)

## Abstract (arXiv)

The objective of this research is to optimize the eleventh iteration of You Only Look Once (YOLOv11) by developing size-specific modified versions of the architecture. These modifications involve pruning unnecessary layers and reconfiguring the main architecture of YOLOv11. Each proposed version is tailored to detect objects of specific size ranges, from small to large. To ensure proper model selection based on dataset characteristics, we introduced an object classifier program. This program identifies the most suitable modified version for a given dataset. The proposed models were evaluated on various datasets and compared with the original YOLOv11 and YOLOv8 models. The experimental results highlight significant improvements in computational resource efficiency, with the proposed models maintaining the accuracy of the original YOLOv11. In some cases, the modified versions outperformed the original model regarding detection performance. Furthermore, the proposed models demonstrated reduced model sizes and faster inference times. Models weights and the object size classifier can be found in this repository