English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Due to the low detection accuracy of YOLOv3 target detection method, this paper proposes an improved target detection method of YOLOv3 remote sensing image. Firstly, the feature extraction network DarkNet53 is strengthened to improve the ability of feature extraction; Secondly, the original Leaky ReLU activation function is replaced by the Mish activation function, thus improving the generalization of the method in this paper; Finally, the Learning rate and BatchSize parameters are modified to prevent overfitting. The remote sensing image datasets of RSOD and TGRS-HRRSD are used in this paper. The Average Precision (AP) results of the method on the RSOD datasets in this paper show that the mAP value is 5.33 percent higher than that of the previous YOLOV3 method. The log average miss-rate (LAMR) results show that the LAMR value is 0.1100 lower than that of the previous YOLOV3 method. The mAP results of the method on the TGRS-HRRSD datasets show that the MAP value is 1.29 percent higher than that of the previous YOLOV3 method, and the LAMR results show that the LAMR value is 0.0338 lower than that of the previous YOLOv3 method.

Improved Object Detection Algorithm of YOLOv3 Remote Sensing Image