Cameras in modern devices such as smartphones, satellites and medical equipment are capable of capturing very high resolution images and videos. Such high-resolution data often need to be processed by deep learning models for cancer detection, automated road navigation, weather prediction, surveillance, optimizing agricultural processes and many other applications. Using high-resolution images and videos as direct inputs for deep learning models creates many challenges due to their high number of parameters, computation cost, inference latency and GPU memory consumption. Simple approaches such as resizing the images to a lower resolution are common in the literature, however, they typically significantly decrease accuracy. Several works in the literature propose better alternatives in order to deal with the challenges of high-resolution data and improve accuracy and speed while complying with hardware limitations and time restrictions. This survey describes such efficient high-resolution deep learning methods, summarizes real-world applications of high-resolution deep learning, and provides comprehensive information about available high-resolution datasets.
翻译:智能手机、卫星和医疗设备等现代设备中的相机能够捕捉非常高分辨率的图像和视频,这类高分辨率数据往往需要通过癌症检测、自动化公路导航、天气预报、监视、优化农业过程和其他许多应用的深层学习模型来处理,使用高分辨率图像和视频作为深层学习模型的直接投入,由于参数、计算成本、推推力潜伏和GPU记忆消耗数量众多,因此带来许多挑战。文献中通常使用简单方法,例如将图像改成低分辨率,但通常会显著降低准确性。一些文献著作提出了更好的替代方法,以应对高分辨率数据的挑战,提高准确性和速度,同时遵守硬件限制和时间限制。本调查描述了这类高效的高分辨率深层学习方法,总结高分辨率深层学习的实际应用,并提供关于现有高分辨率数据集的全面信息。