Car Detection from Very High-Resolution UAV Images Using Deep Learning Algorithms

It is important to determine car density in parking lots, especially in hospitals, large enterprises, and residential areas, which are used intensively, in terms of executing existing management systems and making precise plans for the future. In this study, cars in parking lots were detected using high-resolution unmanned aerial vehicle (UAV) images with deep learning methods. We tested the perfor-mance of the two approaches by determining the number of cars in a parking lot using the You Only Look Once (YOLOv3) and Mask Region-Based Convolutional Neural Networks (Mask R-CNN) ap-proaches as deep learning methods and the deep learning tool of Esri ArcGIS Pro. High-resolution UAV images were processed by photogrammetry and used as input products for the R-CNN and YOLOv3 algorithm. Recall, F1 score, precision ratio/uncertainty accuracy, and average producer accuracy of products automatically extracted with the algorithm were determined as 0.862/0.941, 0.874/0.946, 0.885/0.951, and 0.776/0.897 for R-CNN and YOLOv3, respectively.

Automated summary

Determining car density in parking lots is crucial for executing existing management systems and making precise plans for the future. In this study, high-resolution UAV images and deep learning methods were used to detect cars in parking lots. Two deep learning approaches, YOLOv3 and Mask R-CNN, were tested using the deep learning tool of Esri ArcGIS Pro. The performance of the algorithms was evaluated based on metrics such as recall, F1 score, precision ratio/uncertainty accuracy, and average producer accuracy.