gexudong@st.btbu.edu.cn
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Author 1
Xudong Ge
School of Computer Science and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
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IECE Contributions

Free Access | Research Article | 27 May 2024 | Cited: 5
YOLOv7-Bw: A Dense Small Object Efficient Detector Based on Remote Sensing Image
IECE Transactions on Intelligent Systematics | Volume 1, Issue 1: 30-39, 2024 | DOI:10.62762/TIS.2024.137321
Abstract
In recent years, deep learning techniques have been increasingly applied to the detection of remote sensing images. However, the substantial size variation and dense distribution of objects in these images present significant challenges to detection algorithms. Current methods often suffer from low efficiency, missed detections, and inaccurate bounding boxes. To address these issues, this paper presents an improved YOLO algorithm, YOLOv7-bw, designed for efficient remote sensing image detection, thereby advancing object detection applications in the remote sensing industry. YOLOv7-bw enhances the original SPPCSPC pooling pyramid network by incorporating a Bi-level Routing Attention module, w... More >

Graphical Abstract
YOLOv7-Bw: A Dense Small Object Efficient Detector Based on Remote Sensing Image