Abstract
In this paper, the Faster R-CNN algorithm and YOLOv3 algorithm are researched and practiced based on the remote sensing image data sets. Using the same data sets and hardware environment, it mainly evaluates the average accuracy and the time-consuming for detection of the target objects in the data sets. These algorithm evaluation indicators evaluate the relative applicability of the two algorithms in practical applications. The reasons are also analyzed for the deficiencies of the two algorithms in the target detection process. It is concluded that the Faster R-CNN algorithm is more suitable for practical applications that require higher target detection accuracy, and the YOLOv3 is more suitable for practical applications that require less time-consuming.
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