Enhancing IIoT Vision Data Transmission and Processing via Spatial Difference Attention-Guided Saliency Detection

The swift expansion of the Industrial Internet of Things (IIoT) presents formidable challenges for both data transmission networks and processing units. In this paper, we delve into the issue of accelerating visual data transmission and processing within IIoT systems. Our approach aims to deploy visual saliency detection models in edge data collection devices to empower them with data pre-processing capabilities. To address the challenge of model lightweight deployment on edge devices, we have designed a lightweight saliency detection model. It utilizes our proposed Spatial Difference Attention-based Dynamic Fusion (SADF) module for adaptive detection of salient objects of varying scales. As our model outputs retain only task-relevant information from the images, it maximizes compression on the raw data, significantly reducing the volume of data subsequent tasks and transmission networks need to process. This enhances the speed of data processing. Finally, extensive experimentation across multiple datasets demonstrates that our proposed model achieves a balance between performance and efficiency. Ultimately, this paper presents an effective avenue for efficiently handling data within IIoT systems by combining edge computing nodes with intelligent models.