ArticleSensors (Basel, Switzerland)2022
NRT-YOLO: Improved YOLOv5 Based on Nested Residual Transformer for Tiny Remote Sensing Object Detection.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Focal-HAIN: a lightweight model with adaptive modulation and hierarchical interaction for real-time crop pest and disease monitoring.Frontiers in plant science · 2026Article
- Improved YOLOv9-based remote sensing image detection method.Scientific reports · 2025Article
- An improved YOLOv5n algorithm for detecting surface defects in industrial components.Scientific reports · 2025Article
- SEB-YOLO: An Improved YOLOv5 Model for Remote Sensing Small Target Detection.Sensors (Basel, Switzerland) · 2024Article
- Parameter-Free State Estimation Based on Kalman Filter with Attention Learning for GPS Tracking in Autonomous Driving System.Sensors (Basel, Switzerland) · 2023Article
- AGDF-Net: Attention-Gated and Direction-Field-Optimized Building Instance Extraction Network.Sensors (Basel, Switzerland) · 2023Article
- Deep Learning in Diverse Intelligent Sensor Based Systems.Sensors (Basel, Switzerland) · 2022Review
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5 authors.
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Abstract
To address the problems of tiny objects and high resolution of object detection in remote sensing imagery, the methods with coarse-grained image cropping have been widely studied. However, these methods are always inefficient and complex due to the two-stage architecture and the huge computation for split images. For these reasons, this article employs YOLO and presents an improved architecture, NRT-YOLO. Specifically, the improvements can be summarized as: extra prediction head and related feature fusion layers; novel nested residual Transformer module, C3NRT; nested residual attention module, C3NRA; and multi-scale testing. The C3NRT module presented in this paper could boost accuracy and reduce complexity of the network at the same time. Moreover, the effectiveness of the proposed method is demonstrated by three kinds of experiments. NRT-YOLO achieves 56.9% mAP
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