Energy-Aware NECO for Single-Pass Pixel-wise Out-of-Distribution Detection in Semantic Segmentation

Researchers propose Energy-Aware NECO, a single-pass machine learning method for detecting out-of-distribution data in semantic segmentation tasks. The hybrid approach combines geometric and energy-based scoring to achieve 85.39% detection accuracy while maintaining computational efficiency for edge deployment on mobile robots.

Energy-Aware NECO addresses a critical challenge in deploying semantic segmentation models on resource-constrained devices. Traditional uncertainty quantification methods like Monte Carlo Dropout require multiple forward passes, making them impractical for mobile and edge robotics applications where computational budgets are severely limited. This research bridges that gap by developing a single-pass detector that maintains strong performance without sacrificing efficiency.

The method's innovation lies in its hybrid scoring approach, combining centered NECO geometric ratios from decoder features with logit-based Energy scores. By standardizing both components using in-distribution statistics, the researchers create a robust detector that outperforms individual component approaches. The 85.39% AUROC represents meaningful improvement over pure geometric methods (82.80%) and energy-only approaches (81.71%), while maintaining the single-pass computational advantage critical for edge deployment.

For robotics and autonomous systems industries, this advancement has practical implications. Mobile robots operating in real-world environments encounter distribution shifts—scenarios not represented in training data—where reliable uncertainty estimation prevents catastrophic failures. The ability to detect out-of-distribution pixels without repeated passes reduces latency and power consumption, enabling safer deployment of semantic segmentation in safety-critical applications.

The research validates findings on miniMUAD using true pixel-level labels, providing credible evidence of real-world applicability. As edge AI continues expanding across autonomous vehicles, industrial robotics, and embedded vision systems, efficient uncertainty quantification becomes increasingly valuable. This work demonstrates that performance and efficiency need not be mutually exclusive in modern deep learning architectures.

• →Energy-Aware NECO achieves 85.39% AUROC for out-of-distribution detection with single-pass inference, eliminating computational overhead of ensemble methods.
• →Hybrid scoring combining geometric and energy-based approaches outperforms individual component methods across multiple evaluation metrics.
• →Single-pass design enables deployment on edge devices and mobile robots where computational resources are severely constrained.
• →Method validates on true pixel-level OOD labels from miniMUAD dataset, demonstrating practical robustness for real-world distribution shifts.
• →Efficient uncertainty quantification enables safer autonomous systems by detecting anomalous scenes without sacrificing inference speed.