Hybrid SIFT-SNN for Efficient Anomaly Detection of Traffic Flow-Control Infrastructure

TL;DR

This paper introduces a low-latency, energy-efficient hybrid SIFT-SNN system for real-time structural anomaly detection in traffic infrastructure, demonstrating high accuracy and fast inference on embedded hardware.

Contribution

The paper presents a novel hybrid SIFT-SNN framework that combines spatial feature encoding with neuromorphic processing for efficient anomaly detection in infrastructure.

Findings

Achieves 92.3% classification accuracy

Per-frame inference time of 9.5 ms

Operates with 8.1% spike activity for low power

Abstract

This paper presents the SIFT-SNN framework, a low-latency neuromorphic signal-processing pipeline for real-time detection of structural anomalies in transport infrastructure. The proposed approach integrates Scale-Invariant Feature Transform (SIFT) for spatial feature encoding with a latency-driven spike conversion layer and a Leaky Integrate-and-Fire (LIF) Spiking Neural Network (SNN) for classification. The Auckland Harbour Bridge dataset is recorded under various weather and lighting conditions, comprising 6,000 labelled frames that include both real and synthetically augmented unsafe cases. The presented system achieves a classification accuracy of 92.3% (+- 0.8%) with a per-frame inference time of 9.5 ms. Achieved sub-10 millisecond latency, combined with sparse spike activity (8.1%), enables real-time, low-power edge deployment. Unlike conventional CNN-based approaches, the hybrid SIFT-SNN pipeline explicitly preserves spatial feature grounding, enhances interpretability, supports transparent decision-making, and operates efficiently on embedded hardware. Although synthetic augmentation improved robustness, generalisation to unseen field conditions remains to be validated. The SIFT-SNN framework is validated through a working prototype deployed on a consumer-grade system and framed as a generalisable case study in structural safety monitoring for movable concrete barriers, which, as a traffic flow-control infrastructure, is deployed in over 20 cities worldwide.