Uncertainty Aware Post-Detection Framework

• The framework integrates statistical uncertainty and domain-specific visual cues to enhance object detection in ambiguous and resource-constrained environments.
• It employs dropout-enabled inference and a lightweight Confidence Refinement Network to dynamically rescore detections, mitigating false positives and negatives.
• Empirical results on models like YOLOv8n demonstrate significant precision and recall improvements, making it ideal for real-time safety and surveillance applications.

An uncertainty aware post-detection framework refers to a class of methods that augment, refine, or filter the outputs of object detection models (or other detection-type systems) by incorporating explicit uncertainty quantification into the post-processing pipeline. Such frameworks address the limitations of conventional post-detection strategies that are often confidence-agnostic or reliant on spatial heuristics alone, thereby improving robustness in ambiguous, noisy, or resource-constrained real-world environments. They are especially pertinent where model capacity is constrained (e.g., compact models for edge or IoT deployment) and where the cost of false alarms or missed detections is high, such as in fire, smoke, and disaster response scenarios.

1. Problem Formulation and Motivation

Traditional post-detection processing—such as Non-Maximum Suppression (NMS) and Soft-NMS—operates strictly on the basis of spatial overlap (Intersection-over-Union, IoU) and baseline confidence scores. This approach is prone to both false positives (retaining detections with high confidence but lacking visual plausibility) and false negatives (suppressing true positives in cases of spatial or visual ambiguity). The problem is exacerbated in settings involving compact deep learning models, where model expressiveness is limited, often resulting in miscalibrated predictions and degraded reliability.

An uncertainty aware post-detection framework directly addresses these shortcomings by integrating statistical uncertainty (estimated at the detection model's output) and domain-specific visual cues into a unified refinement mechanism. This allows for more adaptive and contextually-responsible confidence rescaling, enhancing detection reliability especially in cluttered, ambiguous, or out-of-distribution scenarios (Joshi et al., 11 Oct 2025).

2. Uncertainty Quantification Techniques

A core tenet of such frameworks is the explicit quantification of model uncertainty for each detection. In the considered approach, this is achieved by enabling dropout during inference, even for deterministic compact models (e.g., YOLOv5n, YOLOv8n). For each detection candidate, the multiple stochastic forward passes (or a single-pass with dropout enabled) produce a set of confidence scores whose variance (σ_c²) serves as a proxy for model uncertainty:

$$\sigma_c^2 = \frac{1}{N} \sum_{j=1}^{N} (c_j - \mu_e)^2$$

with μₑ being the average confidence and N the number of passes. This quantification penalizes overconfident predictions and provides an informative basis for post-detection rescoring.

3. Feature Integration and Confidence Refinement Network (CRN)

Beyond raw statistical uncertainty, the framework incorporates domain-relevant visual cues that are characteristic of the detection target (e.g., fire or smoke):

• Color features: Extracted from HSV histograms to capture spectral signatures typical of fire (e.g., high saturation, red and yellow hues).
• Edge features: Using Canny edge detection to assess the smoothness or coherence of object boundaries.
• Texture features: Via Haralick descriptors, quantifying local homogeneity or randomness often associated with smoke versus background.

These features are normalized and concatenated with the raw and uncertainty-expressed confidences to form a composite feature vector,

$f = [c_i, \sigma_c^2, s_i, e_i, t_i]$

for each candidate detection i.

The feature vector is processed by a lightweight Confidence Refinement Network (CRN), with architecture:

• Two fully connected layers (32 neurons, ReLU activation),
• Final sigmoid output layer for the refined confidence (ĉᵢ).

This network can be trained to regress true verification labels or empirically tuned to maximize detection metrics on validation data. Importantly, this stage is implemented as a post-processing block and does not modify the base detector or entail significant computational overhead.

4. Empirical Performance and Efficiency

Experiments on the D-Fire dataset demonstrate substantial performance improvements:

Model	Metric	Baseline	+Uncertainty Aware Post-Detection
YOLOv8n	Precision	0.712	0.845
YOLOv8n	Recall	0.674	0.820
YOLOv8n	mAP₅₀	0.625	0.651
YOLOv5n	Precision	0.703	0.840
YOLOv5n	Recall	0.659	0.818
YOLOv5n	mAP₅₀	0.609	0.641

The computational overhead is moderate; for YOLOv8n, the average inference time increased from 12.78 ms to 20.15 ms per image, and similarly modest increments were observed on YOLOv5n. These results highlight that integrating uncertainty estimation and domain-specific cues can significantly improve detection reliability without prohibitive runtime costs—a crucial factor for UAV, IoT, and edge deployments.

5. Contrasts with Conventional Post-Detection Strategies

Traditional NMS and Soft-NMS methods:

• Rely solely on IoU and fixed thresholds;
• May inadvertently suppress true positives among spatially clustered detections (high recall penalty);
• Cannot incorporate non-spatial, visually anchored information;
• Do not differentiate between certain and uncertain detections when scores are similar.

Uncertainty aware frameworks differ by:

• Penalizing ambiguous detections (high estimated σ_c²),
• Boosting scores for visually consistent candidates (matching color, edge, texture patterns of the target),
• Allowing detection confidence to be dynamically adapted in context,
• Reducing both false positives (by demoting visually implausible or highly uncertain detections) and false negatives (by rescuing true positives that might have been suppressed).

6. Deployment and Application Contexts

The uncertainty aware post-detection approach is particularly well-suited for deployment in:

• UAVs for real-time fire/smoke/safety monitoring in dynamic outdoor environments, where model capacity and compute budgets are constrained.
• CCTV Surveillance where background variability and lighting changes challenge classical detectors.
• IoT Devices requiring energy-efficient, high-precision hazard detection in embedded settings.

The general strategy can be extended to other detection tasks where domain-specific visual attributes are available and uncertainty is a key concern.

7. Summary and Implications

Uncertainty aware post-detection frameworks advance beyond traditional spatial heuristics by leveraging model-driven uncertainty quantification and curated domain visual cues to refine detector outputs. By employing a lightweight, domain-adaptive CRN, these frameworks produce better-calibrated confidence estimates, leading to improved precision, recall, and robustness, especially with compact models deployed in resource-constrained or safety-critical scenarios (Joshi et al., 11 Oct 2025). This design paradigm sets a precedent for adaptive and robust post-processing across a spectrum of vision-based detection tasks, aligning model predictions more closely with both statistical and physical interpretability.