A Learned Image Compression Method for Electricity Tower Monitoring Based on the Transformer-CNN-Based Network

The way to monitor the safety of infrastructure facilities such as power towers by human beings on the ground faces great risks under extreme environmental and climatic conditions. Therefore, automatic, real-time and long-term monitoring of power towers in remote areas in the field through sensors, network communication and other technologies is the trend of today’s technology development. However, when the real-time monitoring of high-definition images are captured by the camera and sent to the server for subsequent processing and analysis, the sheer volume of real-time image data causes pressure on the transmission network and the server side. Considering that in the real-time application of remote monitoring technology, the monitoring data obtained from sensors has redundant information, such as similar structure and repetitive background. We only need to extract the image data of the object of interest and compress it before transmission, therefore the image data is significantly transmitted to the server side, improving the efficiency of both network transmission and data processing. In this paper, we propose a learned image compression model by integrating a ResNet50 model and a Transformer-CNN-based network to reduce the image data that needs to be transmitted through the network and processed on the server side. The real-time image data is first sent to the ResNet50 model to extract objects of interest, which are then compressed by the Transformer-CNN network to realize remote monitoring by Learned Image Compression (LIC) methods and communication techniques. Experimental results based on datasets collected in real-world scenarios indicate that the proposed solution effectively improves the compression performance compared to state-of-the-art methods. The average improvements in PSNR and MS-SSIM metrics are over 30 \(\%\) .