Towards Higher Information Density in Image Transmission: Learned Image Compression for Construction Site Monitoring

The digital transformation in the Architecture, Engineering, and Construction (AEC) sector underscores the growing need for efficient data transmission, especially in computer vision tasks that depend on the transfer of large volumes of images. In this work, a novel method is introduced to enhance data transmission efficiency in an edge-cloud coordinated architecture using Learned Image Compression (LIC). By integrating the LIC model with multiple downstream task models (Mask R-CNN and Faster R-CNN), the proposed framework aligns their respective latent features, resulting in a task-oriented LIC model that optimises compression for specific tasks. The approach increases the proportion of task-relevant information—referred to as information density—in the transmitted bitstream. Experimental results demonstrate that this method significantly reduces data transmission load while concentrating the transmitted bits on regions essential for downstream tasks, all without a notable decrease in task accuracy.