Identifying Planetary Nebulae Candidates from IPHAS and VPHAS+ Surveys via YOLO Deep Learning Models
Abstract
We present a deep learning detection method capable of effectively identifying planetary nebulae (PNe) on IPHAS and VPHAS+ surveys. Our detection pipeline is based on the YOLO architecture and is adaptable to other datasets. Using the HiPS system, we preprocess and augment the r , i , and H α bands to produce two red, green, and blue (RGB) image datasets: (1) 640 × 640 px images at full resolution, and (2) 1 280 × 1 280 px images designed to capture extended objects up to 0.2 deg. Images of ∼2600 PNe are labelled using the HASH database. The loss function and evaluation metrics are adapted to the diffuse and low-contrast nature of PNe; a very low intersection over union (IoU) threshold (1%) is used. A k -fold cross validation shows that the YOLOv12m model achieves an average precision of 0.979 on the first dataset. At a confidence threshold of 0.15, it achieves a precision rate of 98.7% and a recall rate of 97.4%. We deployed our system (on 1280 × 1280 images) on the full surveys’ coverage (∼3755 deg 2 ), and present a preliminary analysis of the detection results. The system is robust against artifacts such as CCD corner defects and HiPS tile joints. Our convolutional-neural-network-based model achieved excellent results on the test set, comparable to—or exceeding—that of a recent Swin Transformer approach. However, a challenge persists when applying deep learning models to extensive and densely populated Galactic fields: the development of an automated system that can effectively discriminate PNe from mimics that human experts are able to distinguish (e.g., H II regions). We outline possible improvements for future work.
Versions
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BibTeX
@article{serandour2026identifying,
title = {{Identifying Planetary Nebulae Candidates from IPHAS and VPHAS+ Surveys via YOLO Deep Learning Models}},
author = {Titouan Sérandour and Anne-Laure Méalier and Bruno Colicchio and Dominique Daniel and Philippe Gilberton and Philippe Oros and Thomas Boch and Djaffar Ould Abdeslam},
journal = {The Astronomical Journal},
year = {2026},
month = sep,
doi = {10.3847/1538-3881/aea417},
url = {https://hal.science/hal-05770740v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Identifying Planetary Nebulae Candidates from IPHAS and VPHAS+ Surveys via YOLO Deep Learning Models
AU - Titouan Sérandour
AU - Anne-Laure Méalier
AU - Bruno Colicchio
AU - Dominique Daniel
AU - Philippe Gilberton
AU - Philippe Oros
AU - Thomas Boch
AU - Djaffar Ould Abdeslam
PY - 2026
DA - 2026/09/29
JO - The Astronomical Journal
DO - 10.3847/1538-3881/aea417
UR - https://hal.science/hal-05770740v1
AB - We present a deep learning detection method capable of effectively identifying planetary nebulae (PNe) on IPHAS and VPHAS+ surveys. Our detection pipeline is based on the YOLO architecture and is adaptable to other datasets. Using the HiPS system, we preprocess and augment the r , i , and H α bands to produce two red, green, and blue (RGB) image datasets: (1) 640 × 640 px images at full resolution, and (2) 1 280 × 1 280 px images designed to capture extended objects up to 0.2 deg. Images of ∼2600 PNe are labelled using the HASH database. The loss function and evaluation metrics are adapted to the diffuse and low-contrast nature of PNe; a very low intersection over union (IoU) threshold (1%) is used. A k -fold cross validation shows that the YOLOv12m model achieves an average precision of 0.979 on the first dataset. At a confidence threshold of 0.15, it achieves a precision rate of 98.7% and a recall rate of 97.4%. We deployed our system (on 1280 × 1280 images) on the full surveys’ coverage (∼3755 deg 2 ), and present a preliminary analysis of the detection results. The system is robust against artifacts such as CCD corner defects and HiPS tile joints. Our convolutional-neural-network-based model achieved excellent results on the test set, comparable to—or exceeding—that of a recent Swin Transformer approach. However, a challenge persists when applying deep learning models to extensive and densely populated Galactic fields: the development of an automated system that can effectively discriminate PNe from mimics that human experts are able to distinguish (e.g., H II regions). We outline possible improvements for future work.
SN - 0004-6256
SN - 1538-3881
ER -