Multiscale and Direction Target Detecting in Remote Sensing Images via Modified YOLO-v4
Abstrak
Traditional target detection algorithms have difficulty to adapt complex environmental changes and have limited applicable scenarios. However, the deep-learning-based target detection model can automatically learn with strong generalization capability. In this article, we choose a single-stage deep-learning-based target detection model for research based on the model’s real-time processing requirements and to improve the accuracy and the robustness of target detection in remote sensing images. In addition, we improve the YOLOv4 network and present a new approach. First, we propose a classification setting of the nonmaximum suppression threshold to increase the accuracy without affecting the speed. Second, we study the anchor frame allocation problem in YOLOv4 and propose two allocation schemes. The proposed anchor frame scheme also improves the detection performance, and experimental results on the DOTA dataset validate their effectiveness.
Topik & Kata Kunci
Penulis (6)
Zakria Zakria
Jianhua Deng
Rajesh Kumar
Muhammad Saddam Khokhar
Jingye Cai
Jay Kumar
Akses Cepat
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- 2022
- Sumber Database
- DOAJ
- DOI
- 10.1109/JSTARS.2022.3140776
- Akses
- Open Access ✓