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A Deep-Learning Detector via Optimized YOLOv7-bw Architecture for Dense Small Remote-Sensing Targets in Harsh Food Supply Applications
Research Article  ·  Published: 22 March 2025
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Chinese Journal of Information Fusion
Volume 2, Issue 1, 2025: 38-58
Research Article Open Access

A Deep-Learning Detector via Optimized YOLOv7-bw Architecture for Dense Small Remote-Sensing Targets in Harsh Food Supply Applications

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Xuebo Jin Xuebo Jin 1 ORCID
Heran Fu Heran Fu 1
Jianlei Kong Jianlei Kong 1 * ORCID
Huijun Ma Huijun Ma 1 ORCID
Yuting Bai Yuting Bai 1
Tingli Su Tingli Su 1
1 School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
* Corresponding Author: Jianlei Kong, [email protected]
Volume 2, Issue 1
Volume 2, Issue 1 2025
Received: 03 March 2025 Accepted: 19 March 2025 Published: 22 March 2025 CrossMark
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DOI: 10.62762/CJIF.2025.919344
ARK: ark:/57805/cjif.2025.919344
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Article Information

Published in Chinese Journal of Information Fusion
Volume/Issue Volume 2, Issue 1, 2025
Pages 38-58
Cited by 1 (Crossref) 1 (Scopus)

Abstract

With the progressive advancement of remote sensing image technology, its application in the agricultural domain is becoming increasingly prevalent. Both cultivation and transportation processes can greatly benefit from utilizing remote sensing images to ensure adequate food supply. However, such images often exist in harsh environments with many gaps and dense distribution, which poses major challenges to traditional target detection methods. The frequent missed detections and inaccurate bounding boxes severely constrain the further analysis and application of remote sensing images within the agricultural sector. This study presents an enhanced version of the YOLO algorithm, specifically tailored to achieve high-efficiency detection of densely distributed small targets in remote sensing images. We replaced the convolutions with a convolution kernel size of 3 in the last two ELAN modules with DeformableConvNetsv2 so that the backbone can better extract various objects. The proposed detector introduces a Bi-level Routing Attention module to the pooled pyramid SPPCSPC network of YOLOv7, thereby intensifying the attention towards areas of target concentration and augmenting the network's capacity to extract features related to dense small targets through effective feature fusion. Additionally, our approach employs a dynamic non-monotonic WIoUv3 to ensure the loss function of the network, enabling the allocation of the most appropriate gradient gain strategy at each instant and enhancing the network's ability to focus on detecting targets accurately. Finally, through comparative experimentation on the DIOR remote sensing image dataset, our proposed YOLOv7-bw exhibits superior performance with higher [email protected] and [email protected]: 0.95, achieving detection rates of 85.63% and 65.93%, surpassing those of the YOLOv7 detector by 1.93% and 2.03%, respectively, thus substantiating the effectiveness of our algorithmic approach.

Graphical Abstract

A Deep-Learning Detector via Optimized YOLOv7-bw Architecture for Dense Small Remote-Sensing Targets in Harsh Food Supply Applications

Keywords

remote sensing image feature fusion small object detection harsh food supply management deep learning YOLOv7 architecture

Data Availability Statement

Data will be made available on request.

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 62173007, Grant 62203020, Grant 62473008, Grant 62433002, and Grant 62476014; in part by the Beijing Nova Program under Grant 20240484710; in part by the Project of Humanities and Social Sciences (Ministry of Education in China, MOC) under Grant 22YJCZH006; in part by the Beijing Scholars Program under Grant 099; in part by the Project of ALL China Federation of Supply and Marketing Cooperatives under Grant 202407; in part by the Project of Beijing Municipal University Teacher Team Construction Support Plan under Grant BPHR20220104.

Conflicts of Interest

The authors declare no conflicts of interest.

Ethical Approval and Consent to Participate

Not applicable.

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APA Style
Jin, X., Fu, H., Kong, J., Ma, H., Bai, Y., & Su, T. (2025). A Deep-Learning Detector via Optimized YOLOv7-bw Architecture for Dense Small Remote-Sensing Targets in Harsh Food Supply Applications. Chinese Journal of Information Fusion, 2(1), 38–58. https://doi.org/10.62762/CJIF.2025.919344
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TY  - JOUR
AU  - Jin, Xuebo
AU  - Fu, Heran
AU  - Kong, Jianlei
AU  - Ma, Huijun
AU  - Bai, Yuting
AU  - Su, Tingli
PY  - 2025
DA  - 2025/03/22
TI  - A Deep-Learning Detector via Optimized YOLOv7-bw Architecture for Dense Small Remote-Sensing Targets in Harsh Food Supply Applications
JO  - Chinese Journal of Information Fusion
T2  - Chinese Journal of Information Fusion
JF  - Chinese Journal of Information Fusion
VL  - 2
IS  - 1
SP  - 38
EP  - 58
DO  - 10.62762/CJIF.2025.919344
UR  - https://www.icck.org/article/abs/CJIF.2025.919344
KW  - remote sensing image
KW  - feature fusion
KW  - small object detection
KW  - harsh food supply management
KW  - deep learning
KW  - YOLOv7 architecture
AB  - With the progressive advancement of remote sensing image technology, its application in the agricultural domain is becoming increasingly prevalent. Both cultivation and transportation processes can greatly benefit from utilizing remote sensing images to ensure adequate food supply. However, such images often exist in harsh environments with many gaps and dense distribution, which poses major challenges to traditional target detection methods. The frequent missed detections and inaccurate bounding boxes severely constrain the further analysis and application of remote sensing images within the agricultural sector. This study presents an enhanced version of the YOLO algorithm, specifically tailored to achieve high-efficiency detection of densely distributed small targets in remote sensing images. We replaced the convolutions with a convolution kernel size of 3 in the last two ELAN modules with DeformableConvNetsv2 so that the backbone can better extract various objects. The proposed detector introduces a Bi-level Routing Attention module to the pooled pyramid SPPCSPC network of YOLOv7, thereby intensifying the attention towards areas of target concentration and augmenting the network's capacity to extract features related to dense small targets through effective feature fusion. Additionally, our approach employs a dynamic non-monotonic WIoUv3 to ensure the loss function of the network, enabling the allocation of the most appropriate gradient gain strategy at each instant and enhancing the network's ability to focus on detecting targets accurately. Finally, through comparative experimentation on the DIOR remote sensing image dataset, our proposed YOLOv7-bw exhibits superior performance with higher [email protected] and [email protected]: 0.95, achieving detection rates of 85.63% and 65.93%, surpassing those of the YOLOv7 detector by 1.93% and 2.03%, respectively, thus substantiating the effectiveness of our algorithmic approach.
SN  - 2998-3371
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -
BibTeX Format
Compatible with LaTeX, BibTeX, and other reference managers
@article{Jin2025A,
  author = {Xuebo Jin and Heran Fu and Jianlei Kong and Huijun Ma and Yuting Bai and Tingli Su},
  title = {A Deep-Learning Detector via Optimized YOLOv7-bw Architecture for Dense Small Remote-Sensing Targets in Harsh Food Supply Applications},
  journal = {Chinese Journal of Information Fusion},
  year = {2025},
  volume = {2},
  number = {1},
  pages = {38-58},
  doi = {10.62762/CJIF.2025.919344},
  url = {https://www.icck.org/article/abs/CJIF.2025.919344},
  abstract = {With the progressive advancement of remote sensing image technology, its application in the agricultural domain is becoming increasingly prevalent. Both cultivation and transportation processes can greatly benefit from utilizing remote sensing images to ensure adequate food supply. However, such images often exist in harsh environments with many gaps and dense distribution, which poses major challenges to traditional target detection methods. The frequent missed detections and inaccurate bounding boxes severely constrain the further analysis and application of remote sensing images within the agricultural sector. This study presents an enhanced version of the YOLO algorithm, specifically tailored to achieve high-efficiency detection of densely distributed small targets in remote sensing images. We replaced the convolutions with a convolution kernel size of 3 in the last two ELAN modules with DeformableConvNetsv2 so that the backbone can better extract various objects. The proposed detector introduces a Bi-level Routing Attention module to the pooled pyramid SPPCSPC network of YOLOv7, thereby intensifying the attention towards areas of target concentration and augmenting the network's capacity to extract features related to dense small targets through effective feature fusion. Additionally, our approach employs a dynamic non-monotonic WIoUv3 to ensure the loss function of the network, enabling the allocation of the most appropriate gradient gain strategy at each instant and enhancing the network's ability to focus on detecting targets accurately. Finally, through comparative experimentation on the DIOR remote sensing image dataset, our proposed YOLOv7-bw exhibits superior performance with higher [email protected] and [email protected]: 0.95, achieving detection rates of 85.63\% and 65.93\%, surpassing those of the YOLOv7 detector by 1.93\% and 2.03\%, respectively, thus substantiating the effectiveness of our algorithmic approach.},
  keywords = {remote sensing image, feature fusion, small object detection, harsh food supply management, deep learning, YOLOv7 architecture},
  issn = {2998-3371},
  publisher = {Institute of Central Computation and Knowledge}
}

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