Identifying Planetary Nebulae Candidates from IPHAS and VPHAS+ Surveys via YOLO Deep Learning Models
Year: 2026
Type: article
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.
Source: The Astronomical Journal
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $4,499
OpenAlex ID: W7214752485