📝 SummarXiv

Abstract

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 since these methods are often labor-intensive, costly, and ineffective for modern farming systems. To overcome this, there is a critical need for intelligent, autonomous solutions which require accurate and efficient deer detection. 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 on-field deployability of deer detection systems. Addressing this gap, this study presents a comprehensive evaluation of state-of-the-art deep learning models for deer detection in challenging real-world scenarios. The contributions of this work are threefold. First, we introduce a curated, publicly available dataset of 3,095 annotated images with bounding-box annotations of deer, derived from the Idaho Cameratraps project. Second, we provide an extensive comparative analysis of 12 model variants across four recent YOLO architectures(v8, v9, v10, and v11). Finally, we benchmarked performance on a high-end NVIDIA RTX 5090 GPU and evaluated on two representative edge computing platforms: Raspberry Pi 5 and NVIDIA Jetson AGX Xavier. Results show that the real-time detection is not feasible in Raspberry Pi without hardware-specific model optimization, while NVIDIA Jetson provides greater than 30 FPS with GPU-accelerated inference on '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 (AP@.5 > 0.85) and computational efficiency (FPS > 30). To support further research, both the source code and datasets are publicly available at https://github.com/WinnerBishal/track-the-deer.