Vehicle Detection and Counting from VHR Satellite Images: Efforts and Open Issues

Abstract

Detection of new infrastructures (commercial, logistics, industrial or residential) from satellite images constitutes a proven method to investigate and follow economic and urban growth. The level of activities or exploitation of these sites may be hardly determined by building inspection, but could be inferred from vehicle presence from nearby streets and parking lots. We present in this paper two deep learningbased models for vehicle counting from optical satellite images coming from the Pleiades sensor at 50-cm spatial resolution. Both segmentation (Tiramisu) and detection (YOLO) architectures were investigated. These networks were adapted, trained and validated on a data set including 87k vehicles, annotated using an interactive semi-automatic tool developed by the authors. Experimental results show that both segmentation and detection models could achieve a precision rate higher than 85 % with a recall rate also high (76.4 % and 71.9 % for Tiramisu and YOLO respectively).

YOLO-Fine: One-Stage Detector of Small Objects Under Various Backgrounds in Remote Sensing Images

“…It was collected from WorldView-3 satellites at 30-cm spatial resolution (ground sample distance). A total number of 60 classes are available, but since we focus here on small objects, we gather 19 classes of vehicles, including {17, 18,19,20,21,23,24,26,27,28,32,41,60,62,63,64,65, 66, 91} (these numbers correspond to the initial classes from the original XVIEW data) to create only one vehicle class. Our purpose is not to achieve state-of-the-art detection rate on the XVIEW dataset, but to experiment and validate the capacity of YOLO-fine to detect vehicles from such high resolution satellite images.…”

“…[19][20][21]; detecting and tracking moving objects (such as vehicles, ships, etc.) [22][23][24][25]; and, detecting endangered species (e.g., wildlife animals, sea mammals, etc.) [26,27].…”

Mutual Guidance Meets Supervised Contrastive Learning: Vehicle Detection in Remote Sensing Images

“…Deep-learning-based vehicle detection from aerial and satellite images has been an active research topic in remote sensing for Earth observation within the last decade due to intrinsically challenging natures such as intricately small vehicle sizes, various types and orientations, heterogeneous backgrounds, etc. General approaches include adapting stateof-the-art detectors from the computer vision community to apply to Earth observation context [11,23,24]. Similar to the general object detection task [25], most of the proposed methods could be divided into one-stage and two-stage approaches and are generally based on anchor box prediction.…”

“…In the proposed YOLO-fine [28] and YOLO-RTUAV [29] models, the authors attempted to remove unnecessary network layers from the backbones of YOLOv3 and YOLOv4-tiny, respectively, while adding some others to focus on small object searching. In [23], the Tiramisu segmentation model as well as the YOLOv3 detector were experimented and compared for their capacity to detect very small vehicles from 50-cm Pleiades satellite images. The authors finally proposed a late fusion technique to obtain the combined benefits from both models.…”

YOLO-Extract: Improved YOLOv5 for Aircraft Object Detection in Remote Sensing Images

“…YOLO has become prevailed in target detection tasks, from automatic driving to medical image processing. Alice Froidevaux et al used YOLO to detect vehicles through satellite images [3]; Sudipto Paul et al applied YOLO to brain cancer recognition on MRI images [13]; Ethan Grooby et al explored automated facial landmark detection using YOLO [7]. Although YOLO has achieved great success in object detection tasks, capturing objects from images with noises is still a great challenge.…”