ArticleSensors (Basel, Switzerland)2023
RSI-YOLO: Object Detection Method for Remote Sensing Images Based on Improved YOLO.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 47 citations in OpenAlex.
- PIR-SBFR: A Calibration-Free Sensor-to-Inference Routing Interface for Aerial Optical Object Detection.Sensors (Basel, Switzerland) · 2026Article
- FAMA-DET: A Frequency-Domain Adaptive Multi-Scale Attention Detection Network for Aircraft Target Detection in Optical Remote Sensing Images.Sensors (Basel, Switzerland) · 2026Article
- YOLOv8s-WAMNet: enhancing robust vehicle detection under adverse weather via hybrid attention and multi-scale fusion in real time.Scientific reports · 2026Article
- EFEN-YOLOv8: Surface defect detection network based on spatial feature capture and multi-level weighted attention.PloS one · 2026Article
- Improved YOLOv9-based remote sensing image detection method.Scientific reports · 2025Article
- A Lightweight Person Detector for Surveillance Footage Based on YOLOv8n.Sensors (Basel, Switzerland) · 2025Article
- CsDETC: detection and counting of small targetFrontiers in cellular and infection microbiology · 2025Article
- Research on the Method of Foreign Object Detection for Railway Tracks Based on Deep Learning.Sensors (Basel, Switzerland) · 2024Article
- A New Lunar Lineament Extraction Method Based on Improved UNet++ and YOLOv5.Sensors (Basel, Switzerland) · 2024Article
- MAR-YOLOv9: A multi-dataset object detection method for agricultural fields based on YOLOv9.PloS one · 2024Article
- Real-Time Detection of an Undercarriage Based on Receptive Field Blocks and Coordinate Attention.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
With the continuous development of deep learning technology, object detection has received extensive attention across various computer fields as a fundamental task of computational vision. Effective detection of objects in remote sensing images is a key challenge, owing to their small size and low resolution. In this study, a remote sensing image detection (RSI-YOLO) approach based on the YOLOv5 target detection algorithm is proposed, which has been proven to be one of the most representative and effective algorithms for this task. The channel attention and spatial attention mechanisms are used to strengthen the features fused by the neural network. The multi-scale feature fusion structure of the original network based on a PANet structure is improved to a weighted bidirectional feature pyramid structure to achieve more efficient and richer feature fusion. In addition, a small object detection layer is added, and the loss function is modified to optimise the network model. The experimental results from four remote sensing image datasets, such as DOTA and NWPU-VHR 10, indicate that RSI-YOLO outperforms the original YOLO in terms of detection performance. The proposed RSI-YOLO algorithm demonstrated superior detection performance compared to other classical object detection algorithms, thus validating the effectiveness of the improvements introduced into the YOLOv5 algorithm.
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