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RepDarkNet: A Multi-Branched Detector for Small-Target Detection in Remote Sensing Images
| Content Provider | MDPI |
|---|---|
| Author | Zhou, Liming Zheng, Chang Yan, Haoxin Zuo, Xianyu Liu, Yang Qiao, Baojun Yang, Yong |
| Copyright Year | 2022 |
| Description | Recent years have seen rapid progress in target-detection missions, whereas small targets, dense target distribution, and shadow occlusion continue to hinder progress in the detection of small targets, such as cars, in remote sensing images. To address this shortcoming, we propose herein a backbone feature-extraction network called “RepDarkNet” that adds several convolutional layers to CSPDarkNet53. RepDarkNet considerably improves the overall network accuracy with almost no increase in inference time. In addition, we propose a multi-scale cross-layer detector that significantly improves the capability of the network to detect small targets. Finally, a feature fusion network is proposed to further improve the performance of the algorithm in the AP@0.75 case. Experiments show that the proposed method dramatically improves detection accuracy, achieving AP = 75.53% for the Dior-vehicle dataset and mAP = 84.3% for the Dior dataset, both of which exceed the state-of-the-art level. Finally, we present a series of improvement strategies that justifies our improvement measures. |
| Starting Page | 158 |
| e-ISSN | 22209964 |
| DOI | 10.3390/ijgi11030158 |
| Journal | ISPRS International Journal of Geo-Information |
| Issue Number | 3 |
| Volume Number | 11 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-02-22 |
| Access Restriction | Open |
| Subject Keyword | ISPRS International Journal of Geo-Information Isprs International Journal of Geo-information Imaging Science Remote Sensing Deep Learning Convolutional Neural Network Backbone Network Target Detection Remote Sensing Images |
| Content Type | Text |
| Resource Type | Article |
