Peak-CNNv2: A Deformable FPN Architecture with YOLO-style Modules for Accurate Particle Localization

Accurate detection of particle clusters in high-density 2D images is crucial for high-energy physics, medical imaging, and astrophysics. Traditional clustering methods, such as K-means and DBSCAN, often fail with overlapping or closely spaced particles, causing false detections and errors in centroid-of-gravity (COG) measurements. To address these challenges, we propose Peak-CNNv2, a lightweight convolutional neural network that directly predicts cluster peak positions for precise localization. The network incorporates YOLO-style convolutional blocks—drawing on YOLO’s (You Only Look Once) design principle of efficient local feature extraction to enhance responsiveness to dense particle distributions—alongside deformable convolutions and a multi-scale Feature Pyramid Network (FPN) to improve handling of overlapping and irregular clusters. Compared to heavier models, Peak-CNNv2 achieves high accuracy with manageable computational cost. Experimental results demonstrate that Peak-CNNv2 achieves an F1-score exceeding 93% for 1–60 clusters and robust performance under overlapping conditions. It also reduces centroid errors and minimizes false positives. By integrating deep learning with multi-scale feature fusion and YOLO-inspired efficient convolution design, Peak-CNNv2 provides a scalable and accurate solution for detecting particles in complex and noisy data. This approach advances image analysis in physics, medical imaging, and astrophysics, enabling reliable processing of dense datasets.