Extended Evaluation of SnowPole Detection for Machine-Perceivable Infrastructure for Nordic Winter Conditions: A Comparative Study of Object Detection Models

In this study, we present an extensive evaluation of state-of-the-art YOLO object detection architectures for identifying snow poles in LiDAR-derived imagery captured under challenging Nordic conditions. Building upon our previous work on the SnowPole Detection dataset [1] and our LiDAR–GNSS-based localization framework [2], we expand the benchmark to include six YOLO models—YOLOv5s, YOLOv7-tiny, YOLOv8n, YOLOv9t, YOLOv10n, and YOLOv11n—evaluated across multiple input modalities. Specifically, we assess single-channel modalities (Reflectance, Signal, Near-Infrared) and six pseudo-color combinations derived by mapping these channels to RGB representations. Each model’s performance is quantified using Precision, Recall, mAP@50, mAP@50–95, and GPU inference latency. To facilitate systematic comparison, we define a composite Rank Score that integrates detection accuracy and real-time performance in a weighted formulation. Experimental results show that YOLOv9t consistently achieves the highest detection accuracy, while YOLOv11n provides the best trade-off between accuracy and inference speed, making it a promising candidate for real-time applications on embedded platforms. Among input modalities, pseudo-color combinations—particularly those fusing Near-Infrared, Signal, and Reflectance channels—outperformed single modalities across most configurations, achieving the highest Rank Scores and mAP metrics. Therefore, we recommend using multimodal LiDAR representations such as Combination 4 and Combination 5 to maximize detection robustness in practical deployments. All datasets, benchmarking code, and trained models are publicly avail- able to support reproducibility and further research through our GitHub repository (a).

References
[1] Durga Prasad Bavirisetti, Gabriel Hanssen Kiss, Petter Arnesen, Hanne Seter, Shaira Tabassum, and Frank Lindseth. Snowpole detection: A comprehensive dataset for detection and localization using lidar imaging in nordic winter conditions. Data in Brief, 59:111403, 2025.
[2] Durga Prasad Bavirisetti, Gabriel Hanssen Kiss, and Frank Lindseth. A pole detection and geospatial localization framework using lidar-gnss data fusion. In 2024 27th International Conference on Information Fusion (FUSION), pages 1–8. IEEE, 2024.
(a) https://github.com/MuhammadIbneRafiq/Extended-evaluation-snowpole-lidar-dataset