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IECE Transactions on Intelligent Systematics, 2024, Volume 1, Issue 1: 30-39

Free Access | Research Article | 27 May 2024 | Cited: 5
1 School of Computer Science and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
* Corresponding Author: Huijun Ma, [email protected]
Received: 16 February 2024, Accepted: 19 May 2024, Published: 27 May 2024  

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, which focuses on densely populated target areas to improve the network's feature extraction capabilities. Additionally, it introduces a dynamic non-monotonic WIoUv3 loss function to replace the original CIoU loss function. This substitution ensures that the loss function's gradient allocation strategy aligns more effectively with the current detection scenario, enhancing the network's focus on the detection object. Through comparative experiments on the DIOR remote sensing image dataset, we found that YOLOv7-bw achieved a high [email protected] of 85.63% and a high [email protected]:0.95 of 65.93%, surpassing the previous results of 83.7% and 63.9% by approximately 1.93% and 2.03%, respectively. Moreover, compared with commonly used algorithms, YOLOv7-bw demonstrated superior performance, thereby validating the feasibility and enhanced applicability of our proposed algorithm for remote sensing image detection.

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

Keywords
Remote Sensing Image
YOLO
Object Detection
mAP

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Cite This Article
APA Style
Jin, X., Tong, A., Ge, X., Ma, H., Li, J., Fu, H., & Gao, L. (2024). YOLOv7-Bw: A Dense Small Object Efficient Detector Based on Remote Sensing Image. IECE Transactions on Intelligent Systematics, 1(1), 30-39. https://doi.org/10.62762/TIS.2024.137321

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