# A Comprehensive Evaluation of YOLO-based Deer Detection Performance on Edge Devices

arXiv: https://arxiv.org/abs/2509.20318 · HTML: https://mlsanity.com/paper/2509.20318/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 54 references that are in the map.
- First posted on arXiv 24 Sep 2025.

## Builds on (most cited first, 13 of 13)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Towards Out-Of-Distribution Generalization: A Survey](https://mlsanity.com/paper/2108.13624/) (2021)
- [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://mlsanity.com/paper/2402.13616/) (2024)
- [YOLOv11: An Overview of the Key Architectural Enhancements](https://mlsanity.com/paper/2410.17725/) (2024)
- [YOLOv12: Attention-Centric Real-Time Object Detectors](https://mlsanity.com/paper/2502.12524/) (2025)
- [A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS](https://mlsanity.com/paper/2304.00501/) (2023)
- [What is YOLOv8: An In-Depth Exploration of the Internal Features of the Next-Generation Object Detector](https://mlsanity.com/paper/2408.15857/) (2024)
- [YOLOv5, YOLOv8 and YOLOv10: The Go-To Detectors for Real-time Vision](https://mlsanity.com/paper/2407.02988/) (2024)
- [A Comprehensive Evaluation of Deep Learning Object Detection Models on Heterogeneous Edge Devices](https://mlsanity.com/paper/2409.16808/) (2024)
- [Improving the Reproducibility of Deep Learning Software: An Initial Investigation through a Case Study Analysis](https://mlsanity.com/paper/2505.03165/) (2025)
- [Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches](https://mlsanity.com/paper/2506.00154/) (2025)
- [Efficient Endangered Deer Species Monitoring with UAV Aerial Imagery and Deep Learning](https://mlsanity.com/paper/2506.00164/) (2025)

## Built on by (most cited first, 1 of 1)

- [Resource-Constrained UAV-Based Weed Detection for Site-Specific Management on Edge Devices](https://mlsanity.com/paper/2604.23442/) (2026)

## Abstract (arXiv)

The escalating economic losses in agriculture due to deer intrusion, estimated to be in the hundreds of millions of dollars annually in the U.S., highlight the inadequacy of traditional mitigation strategies such as hunting, fencing, use of repellents, and scare tactics. This underscores a critical need for intelligent, autonomous solutions capable of real-time deer detection and deterrence. But the progress in this field is impeded by a significant gap in the literature, mainly the lack of a domain-specific, practical dataset and limited study on the viability of deer detection systems on edge devices. To address this gap, this study presents a comprehensive evaluation of state-of-the-art deep learning models for deer detection in challenging real-world scenarios. We introduce a curated, publicly available dataset of 3,095 annotated images with bounding box annotations of deer. Then, we provide an extensive comparative analysis of 12 model variants across four recent YOLO architectures (v8 to v11). Finally, we evaluated their performance on two representative edge computing platforms: the CPU-based Raspberry Pi 5 and the GPU-accelerated NVIDIA Jetson AGX Xavier to assess feasibility for real-world field deployment. Results show that the real-time detection performance is not feasible on Raspberry Pi without hardware-specific model optimization, while NVIDIA Jetson provides greater than 30 frames per second (FPS) with 's' and 'n' series models. This study also reveals that smaller, architecturally advanced models such as YOLOv11n, YOLOv8s, and YOLOv9s offer the optimal balance of high accuracy (Average Precision (AP) > 0.85) and computational efficiency (Inference Time < 34 milliseconds).