Bistroń Marta, Piotrowski Zbigniew
Institute of Communication Systems, Faculty of Electronics, Military University of Technology, 00-908 Warsaw, Poland.
Sensors (Basel). 2024 Dec 13;24(24):7977. doi: 10.3390/s24247977.
Image reconnaissance systems are critical in modern applications, where the ability to accurately detect and identify objects is crucial. However, distortions in real-world operational conditions, such as motion blur, noise, and compression artifacts, often degrade image quality, affecting the performance of detection systems. This study analyzed the impact of super-resolution (SR) technology, in particular, the Real-ESRGAN model, on the performance of a detection model under disturbed conditions. The methodology involved training and evaluating the Faster R-CNN detection model with original and modified data sets. The results showed that SR significantly improved detection precision and mAP in most interference scenarios. These findings underscore SR's potential to improve imaging systems while identifying key areas for future development and further research.
图像识别系统在现代应用中至关重要，准确检测和识别物体的能力至关重要。然而，现实世界操作条件下的失真，如运动模糊、噪声和压缩伪像，常常会降低图像质量，影响检测系统的性能。本研究分析了超分辨率（SR）技术，特别是Real-ESRGAN模型，对受干扰条件下检测模型性能的影响。该方法包括使用原始和修改后的数据集训练和评估Faster R-CNN检测模型。结果表明，在大多数干扰场景中，超分辨率显著提高了检测精度和平均精度均值（mAP）。这些发现强调了超分辨率在改善成像系统方面的潜力，同时确定了未来发展和进一步研究的关键领域。
