A Multi-Stage Spike Stream Processing and Image Reconstruction Method for Industrial Applications

With the advancement of new industrialization, production processes or scenarios characterized by automation and intelligence require monitoring by more powerful visual inspection tools to maintain production continuity, safety, and efficiency. Spike vision sensors, with their high temporal resolution and high dynamic range, can capture subtle changes of high-speed moving objects in complex industrial environments, providing precise data support for equipment maintenance, fault detection, and condition monitoring. However, in industrial scenarios, factors such as mechanical vibrations, electromagnetic interference, and extreme strong light affect the quality of sensor data, leading to higher noise levels and increased probability of data loss and errors. To address this, we propose a multi-stage processing method for spike data processing and image reconstruction. It first performs preliminary feature extraction and denoising through an anti-noise feature encoder. Building on this, it integrates features and enhances consistency through spatiotemporal information utilization. Finally, it reconstructs the images through an fine-tuned spike reconstruction model. Experimental results show that our method can effectively reduce noise and improve data quality, achieving good performance on industrial datasets. This provides higher quality data support for downstream tasks such as predictive maintenance and high-speed production inspection in industrial scenarios.