SDPH: a new technique for spatial detection of path holes from huge volume high-resolution raster images in near real-time

Journal of Real-Time Image Processing (2024) 21:70 https://doi.org/10.1007/s11554-024-01451-7 RESEARCH SDPH: a new technique for spatial detection of path holes from huge volume high‑resolution raster images in near real‑time Murat Tasyurek1 Received: 4 January 2024 / Accepted: 3 March 2024 / Published online: 4 April 2024 © The Author(s) 2024 Abstract Detecting and repairing road defects is crucial for road safety, vehicle maintenance, and enhancing tourism on well-maintained roads. However, monitoring all roads by vehicle incurs high costs. With the widespread use of remote sensing technologies, high-resolution satellite images offer a cost-effective alternative. This study proposes a new technique, SDPH, for automated detection of damaged roads from vast, high-resolution satellite images. In the SDPH technique, satellite images are organized in a pyramid grid file system, allowing deep learning methods to efficiently process them. The images, generated as 256 × 256 dimensions, are stored in a directory with explicit location information. The SDPH technique employs a two-stage object detection models, utilizing classical and modified RCNNv3, YOLOv5, and YOLOv8. Classical RCNNv3, YOLOv5, and YOLOv8 and modified RCNNv3, YOLOv5, and YOLOv8 in the first stage for identifying roads, achieving f1 scores of 0.743, 0.716, 0.710, 0.955, 0.958, and 0.954, respectively. When the YOLOv5, with the highest f1 score, was fed to the second stage; modified RCNNv3, YOLOv5, and YOLOv8 detected road defects, achieving f1 scores of 0.957,0.971 and 0.964 in the second process. When the same CNN model was used for road and road defect detection in the proposed SDPH model, classical RCNNv3, improved RCNNv3, classical YOLOv5, improved YOLOv5, classical YOLOv8, improved RCNNv8 achieved micro f1 scores of 0.752, 0.956, 0.726, 0.969, 0.720 and 0.965, respectively. In addition, these models processed 11, 10, 33, 31, 37, and 36 FPS images by performing both stage operations, respectively. Evaluations on geotiff satellite images from Kayseri Metropolitan Municipality, ranging between 20 and 40 gigabytes, demonstrated the efficiency of the SDPH technique. Notably, the modified YOLOv5 outperformed, detecting paths and defects in 0.032 s with the micro f1 score of 0.969. Fine-tuning on TileCache enhanced f1 scores and reduced computational costs across all models. Keywords Near real-time · High-resolution imagery · Pat hole detection · Object detection with spatial location 1 Introduction Damaged paths cause traffic accidents and injuries [1, 2]. Imperfections such as potholes, cracks, or trapezoids can make vehicles difficult to control, distract drivers, and cause them to make sudden maneuvers. Detecting damaged roads improves driver safety and helps prevent accidents [3]. However, it is very costly to constantly check all roads by assigning personnel to detect broken roads. On the other hand, with the widespread use of remote sensing technology in recent years, huge volume and high-resolution images are obtained [4–6]. High-resolution satellite images are * Murat Tasyurek 1 Department of Computer Engineering, Kayseri University, Kayseri, Turkey images obtained from space or the air that show the details of the Earth in high resolution. These images usually have sufficient pixel density to distinguish very small objects or details [7]. However, these images take up a large amount of space on computer systems [8]. Large volumes of data are challenging to handle by classical data processing methods [9, 10]. Technology giants such as ArcGIS and Google Earth cut huge volumes of images into small pieces, allowing users to access these images at high speed [11–13]. Large-volume images are divided into small pieces in the form of small grids and accessed in pyramid grid file format [14]. This operation is called tile [15]. In the tiling process, huge volumes of satellite images are recorded on computer disks as z/x/y.mime type [16]. Z denotes the recording level of the image, x signifies the image’s position along the X-axis, the y-value represents its position along the Y-axis, and the Vol.:(0123456789) 70 Page 2 of 19 mime type specifies the format in which the image is to be stored. These images are produced and saved in 256 × 256 dimensions. When the user wants to access the image in a region, it takes a very long time to take the huge volume image and present the relevant region. Instead, the pyramid grid file system provides high-speed access to users because small-size images in the appropriate location are presented [17, 18]. In recent times, the advanced deep learning technique, a cutting-edge computer technology, has found widespread application in diverse domains such as image classification [19], object tracking [20], and pose estimation [21]. Numerous deep learning methodologies have gained widespread acceptance in this domain. Notably, among these techniques, convolutional neural networks (CNNs) have demonstrated remarkable efficacy, particularly in the realm of image classification [22]. Detection of road disturbances is a classification problem in computer vision. Within the literature, research on the identification of road disturbances through onboard cameras often employs CNN-based methods [23–25]. In this investigation, we propose a new method, referred to as SDPH, for identifying road disturbances along with their spatial coordinates within huge volumes and high-resolution satellite imagery. CNN models could not process satellite images ranging in size from 20 to 40 gigabits. In the suggested SDPH methodology, the conversion of these images into a pyramid grid file format is facilitated through the GeoServerTileCache software, employing an open-source strategy. This format ensures compatibility for processing the images through CNN models. In the satellite images, there are disturbances in the soil. In the recommended SDPH technique, a two-stage deep learning technique has been developed to detect only road disturbances. As a deep learning method, RCNNv3 [25, 26], YOLOv5 [24, 27], and YOLOv8 [28, 29] were used. 1.1 Contributions – A new technique, SDPH, is recommended to detect road disturbances and their corresponding spatial locations within vast volumes of high-resolution satellite imagery. – The achievement of the recommended technique has been tested on real satellite image data with sizes between 20 and 40 gigabytes. – A pyramid grid file system using GeoServer-TileCache has been recommended so that CNN models can process huge volume satellite images. – Thanks to the recommended pyramid grid technique, a huge volume satellite image that occupies at least 20 gigabytes of space and, therefore cannot be processed has been converted into small and processable images, the largest of which is 23 kilobytes. Journal of Real-Time Image Processing (2024) 21:70 – A fine-tuning technique has been developed to improve the object detection achievement of the recommended SDPH te (...truncated)