Two-Stage Detection Framework

• The two-stage detection framework is a modular method that first generates high-recall candidate regions and then refines them for improved precision.
• It underpins diverse applications—from object detection to biomedical analysis—by decoupling localization from classification.
• It employs distinct loss functions and optimization strategies in each stage to balance efficiency, scalability, and accuracy across various domains.

A two-stage detection framework is a class of machine learning architectures that decomposes a detection or decision task into a sequential pipeline of two distinct, specialized modules or “stages." The first stage typically generates candidate regions, features, or proposals in a coarse or unsupervised manner—often maximizing recall or providing broad coverage—while the second stage performs a more targeted, refined, or supervised discrimination or regression, commonly maximizing precision or reducing false positives. This staged formulation underpins many state-of-the-art methods in object detection, anomaly detection, forgery detection, federated learning, and beyond, owing to its ability to decouple localization from classification, exploit task-specific priors, and modularize optimization objectives.

1. General Principles and Taxonomy

A canonical two-stage detection framework consists of:

• Stage I (Proposal/Representation): Generates a high-recall, potentially noisy set of candidate regions, representations, or anomaly indices. This module is typically lightweight or uses unsupervised/detector-specific logic: e.g., region proposal networks (RPNs) (Guo, 24 May 2024), unsupervised autoencoders (Kuili et al., 25 Jan 2025), anatomical candidate extractors in medical imaging (K et al., 2020), or dense keypoint proposals (Duan et al., 2020).
• Stage II (Verification/Refinement): Consumes the candidates from Stage I and applies stricter or more specific supervision for discrimination, precision, or fine-grained adjustment. This module may use fully connected networks, high-capacity CNNs, attention modules, or statistical models, and is often responsible for false positive suppression and calibration.

This staged approach is applicable in:

• Object detection (anchor-based [Faster R-CNN], anchor-free [CPN, (Duan et al., 2020)], transformer-based [DEYO, (Ouyang, 2022)]).
• Anomaly/outlier detection (deep autoencoding + time-series modeling (Jeong et al., 2022), kernel PCA + local outlyingness (Tamamori, 28 Oct 2025)).
• Biomedical detection (mitosis, mass detection: cascaded detector + classifier (Piansaddhayanon et al., 2022, Xiao et al., 1 Sep 2025)).
• Forgery detection (dense patch-wise detection + global classification (Zhang et al., 2022, Liu et al., 2020)).
• Federated/heterogeneous data (unsupervised representation + supervised classifier, both FL (Kuili et al., 25 Jan 2025)).

2. Algorithmic and Architectural Instantiations

Object Detection

• Proposal mechanisms:
  • Anchor-based RPNs predict objectness and bounding boxes using convolutional feature maps (e.g., in Faster R-CNN (Guo, 24 May 2024)).
  • Anchor-free approaches (e.g., CornerNet, CPN (Duan et al., 2020)) leverage detection of keypoints and spatial relationships to form proposals.
  • Dense proposal networks, such as YOLO, may be re-purposed as Stage I in hybrid frameworks (e.g., DEYO (Ouyang, 2022)).
• Second-stage classifiers/heads:
  • Fully connected layers for class logits and bounding-box regression (Duan et al., 2020, Guo, 24 May 2024).
  • Attention mechanisms, transformer decoders (e.g., POST-DEYO, DINO (Ouyang, 2022)).
  • Application-specific classifiers (e.g., ConvNeXt in mitosis detection (Xiao et al., 1 Sep 2025)).

Anomaly and Outlier Detection

• A first-stage autoencoder or kernel mapping provides robust, type-specific anomaly candidates (Jeong et al., 2022, Tamamori, 28 Oct 2025).
• Local, context-aware scoring or higher-level sequence modeling (e.g., LSTM, clustering, SVM-based) filters out residual false positives or exploits temporal structure (Jeong et al., 2022, Tamamori, 28 Oct 2025).

Federated/Jamming Detection

• Unsupervised CAE representation learning: Each federated client extracts disentangled latent codes from local data via a convolutional autoencoder trained using federated averaging (FedAvg) (Kuili et al., 25 Jan 2025).
• Supervised classification head: A shallow FCN classifier is trained (FedProx) on top of the frozen encoder, with data privacy preserved and robust convergence under non-IID partitions (Kuili et al., 25 Jan 2025).

Biomedical and Digital Pathology

• Candidate extraction: Classical segmentation or detection networks (e.g., U-Net, YOLO11x) generate candidate patches covering regions likely to contain salient targets (tumor, cell, mitosis) (K et al., 2020, Xiao et al., 1 Sep 2025).
• Patch-level or image-level classification: Patches are scored by discriminators (EfficientNet, ConvNeXt) or fused by attention/multiple-instance learning, yielding high precision and interpretability (Xiao et al., 1 Sep 2025, K et al., 2020).

3. Mathematical Formulations and Optimization Objectives

Two-stage frameworks are typically governed by distinct loss functions in each stage, tailored to the granularity and supervision available:

• Stage I:
  • Proposal/detection loss: e.g., binary cross-entropy, focal loss, localization loss (smooth L1 or IoU-based) on box proposals (Guo, 24 May 2024, Duan et al., 2020, Ouyang, 2022).
  • Reconstruction loss: MSE or BCE for unsupervised autoencoders (Kuili et al., 25 Jan 2025, Jeong et al., 2022).
• Stage II:
  • Classification loss: cross-entropy, focal, or weighted loss, possibly with contrastive or margin-based objectives (K et al., 2020, Xiao et al., 1 Sep 2025).
  • Regularization terms to encourage feature discrimination, suppress drift, or adapt to distributional shift (e.g., FedProx penalty (Kuili et al., 25 Jan 2025), agreement loss in multimodal fusion (Li et al., 24 Sep 2025)).
• Probabilistic frameworks: Explicitly decouple objectness and class-conditional scores, applying the chain rule: $P(C_k=c) = P(O_k=1)P(C_k=c|O_k=1)$ (Zhou et al., 2021).

End-to-end pseudocode is available in several works, summarizing the communication and optimization steps, particularly for federated or computationally distributed implementations (Kuili et al., 25 Jan 2025, Guo, 24 May 2024).

4. Empirical Impact and performance trade-offs

Empirical studies demonstrate that two-stage frameworks deliver:

• Improved F1 and precision: E.g., precision increased from 0.762 to 0.839 (F1: 0.847→0.882) in mitosis detection by filtering YOLO candidates with a ConvNeXt classifier (Xiao et al., 1 Sep 2025); F1-score gains of 4.8 pp and MAPE reductions of 44.1% in ReCasNet (Piansaddhayanon et al., 2022); SOTA outlier detection AUCs in Two-Stage LKPLO (Tamamori, 28 Oct 2025).
• False positive suppression: Targeted strategies at both training and inference (e.g., PST algorithm (Guo, 24 May 2024), Full-Stage Refined Proposal (Guo et al., 2 Aug 2025)) decrease log-average miss rates by 2–3% on challenging pedestrian benchmarks while holding computational cost nearly constant.
• Efficiency and scalability: Hierarchical cascading (e.g., BLT-net (Dana et al., 2021): computational reduction by 4x–7x with marginal accuracy loss) and federated training with 30 communication rounds (Kuili et al., 25 Jan 2025) enable deployment in resource-constrained or privacy-sensitive environments.

In 3D object detection, two-stage (RoI head + sparse context module) frameworks recover >7% mAP gap relative to efficient single-stage baselines (e.g., 3DPillars (Noh et al., 6 Sep 2025)), while supporting real-time throughput.

A summary of empirical metrics from various domains:

Framework	Domain	Precision	Recall	F1-score	mAP/AP	Notable Metric Improvement
CAE+FCN FL (Kuili et al., 25 Jan 2025)	5G jamming	0.94	0.90	0.92	—	Robust non-IID FL convergence
YOLO11x+ConvNeXt (Xiao et al., 1 Sep 2025)	Mitosis	0.839	0.929	0.882	—	F1 +0.035 vs improved YOLO single-stage
CPN (Duan et al., 2020)	Object Det.	—	—	—	AP=49.2	2–3% AP gain (FPS≈43)
PST (Guo, 24 May 2024)	Pedestrian	—	—	—	MR↓	0.8–2.1% MR reduction at no extra run-time
ReCasNet (Piansaddhayanon et al., 2022)	Pathology	—	—	+4.8 pp	—	F1, 44%↓MAPE in mitotic count
LKPLO (Tamamori, 28 Oct 2025)	Outlier Det.	—	—	—	AUC=0.843	Outperforms kernel & localized RPD

5. Auxiliary Mechanisms and Innovations

Specialized strategies often augment or extend the canonical two-stage pattern:

• Proposal refinement and negative mining: Hard-negative filtering in proposal assignments, integer proposal splitting (Split-proposal FRP), and sampling by classifier disagreement (Guo et al., 2 Aug 2025, Piansaddhayanon et al., 2022).
• Attention and context: Channel/spatial attention in patch-level detectors (Zhang et al., 2022); context-aware memory modules in RoI heads for 3D detection (Noh et al., 6 Sep 2025).
• Multi-modal/multi-resolution adaptation: GMM-based region clustering for small object focus (Koyun et al., 2022), kernel PCA + local adaptive scoring for structured outlier detection (Tamamori, 28 Oct 2025), staged time-series filtering for industrial anomaly detection (Jeong et al., 2022).
• Probabilistic decoupling: Explicitly chaining objectness and conditional class posteriors to enable improved calibration and faster inference (Zhou et al., 2021).

6. Limitations, Open Problems, and Design Guidelines

Two-stage frameworks, while versatile and empirically dominant across a range of domains, inherit several limitations:

• Stage mismatch: Poor proposal quality or domain gap between stages can propagate false positives or negatives, motivating supplementary strategies like window relocation, re-cropping, and classifier-guided proposal filtering (Piansaddhayanon et al., 2022, Guo et al., 2 Aug 2025).
• Computational overhead: Second-stage heads can be bottlenecks; aggressive proposal filtering, proposal merging, and dynamic downscaling are crucial for resource-constrained scenarios (Dana et al., 2021).
• Hyperparameter sensitivity: Selection of thresholds, proposal counts, and loss balancing is dataset- and task-dependent, often requiring ablation studies and grid search (Jeong et al., 2022).
• Privacy and communication: Federated two-stage protocols mitigate but do not fully solve privacy and bandwidth constraints; optimal client selection and early stopping are active research areas (Kuili et al., 25 Jan 2025).

Design guidance includes using a very high-recall, low-cost Stage I followed by aggressive filtering and adaptation in Stage II, matching capacity and supervision to the heterogeneity and complexity of the dataset and deployment setting.

7. Applications and Future Directions

Two-stage frameworks have demonstrated state-of-the-art performance in:

• Vision: General object detection, small object focus, forgery localization, and 3D LiDAR scene understanding (Ouyang, 2022, Koyun et al., 2022, Liu et al., 2020, Noh et al., 6 Sep 2025).
• Medical imaging: Mitosis/nucleus detection, mass localization, scan-level tampering detection (Xiao et al., 1 Sep 2025, K et al., 2020, Zhang et al., 2022).
• Industrial process monitoring: Heterogeneous anomaly filtering and hierarchical time series classification (Jeong et al., 2022).
• Federated security analytics: Privacy-preserving jamming detection and classification (Kuili et al., 25 Jan 2025).
• Multimodal/multisensor fusion: Inconsistency detection and selective integration in affective computing (Li et al., 24 Sep 2025).

Current research trends highlight:

• Strongly integrating architectural innovations in proposal mechanisms, self- and cross-attention, and context-awareness.
• Optimizing communication, computation, and robustness for distributed/federated or edge inference.
• Generalizing proposal-verification and negative mining methods across domains, including in non-vision and highly multimodal settings.

The staged paradigm remains a foundation for hybrid, interpretable, and high-utility detection systems in both centralized and decentralized environments.