ArticleSensors (Basel, Switzerland)2020
Improved YOLO-V3 with DenseNet for Multi-Scale Remote Sensing Target Detection.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
- Application of Improved YOLOv8 Image Model in Urban Manhole Cover Defect Management and Detection: Case Study.Sensors (Basel, Switzerland) · 2025Article
- A Lightweight Multi-Scale Context Detail Network for Efficient Target Detection in Resource-Constrained Environments.Sensors (Basel, Switzerland) · 2025Article
- Steel surface defect detection based on multi-layer fusion networks.Scientific reports · 2025Article
- ARSOD-YOLO: Enhancing Small Target Detection for Remote Sensing Images.Sensors (Basel, Switzerland) · 2024Article
- PDT-YOLO: A Roadside Object-Detection Algorithm for Multiscale and Occluded Targets.Sensors (Basel, Switzerland) · 2024Article
- Optimization of table tennis target detection algorithm guided by multi-scale feature fusion of deep learning.Scientific reports · 2024Article
- YOLO-DRS: A Bioinspired Object Detection Algorithm for Remote Sensing Images Incorporating a Multi-Scale Efficient Lightweight Attention Mechanism.Biomimetics (Basel, Switzerland) · 2023Article
- Research on Safety Helmet Detection Algorithm Based on Improved YOLOv5s.Sensors (Basel, Switzerland) · 2023Article
- YOLOv4 with Deformable-Embedding-Transformer Feature Extractor for Exact Object Detection in Aerial Imagery.Sensors (Basel, Switzerland) · 2023Article
- Deep Learning in Diverse Intelligent Sensor Based Systems.Sensors (Basel, Switzerland) · 2022Review
- Machine-Learning Approach for Automatic Detection of Wild Beluga Whales from Hand-Held Camera Pictures.Sensors (Basel, Switzerland) · 2022Article
- An improved YOLO Nano model for dorsal hand vein detection system.Medical & biological engineering & computing · 2022Review
- Small target detection with remote sensing images based on an improved YOLOv5 algorithm.Frontiers in neurorobotics · 2022Article
- Platelet Detection Based on Improved YOLO_v3.Cyborg and bionic systems (Washington, D.C.) · 2022Article
- Ship Target Detection in Optical Remote Sensing Images Based on Multiscale Feature Enhancement.Computational intelligence and neuroscience · 2022Article
- Aircraft Detection for Remote Sensing Image Based on Bidirectional and Dense Feature Fusion.Computational intelligence and neuroscience · 2021Article
- Design of a Scalable and Fast YOLO for Edge-Computing Devices.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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2 authors.
Funding
Abstract
Remote sensing targets have different dimensions, and they have the characteristics of dense distribution and a complex background. This makes remote sensing target detection difficult. With the aim at detecting remote sensing targets at different scales, a new You Only Look Once (YOLO)-V3-based model was proposed. YOLO-V3 is a new version of YOLO. Aiming at the defect of poor performance of YOLO-V3 in detecting remote sensing targets, we adopted DenseNet (Densely Connected Network) to enhance feature extraction capability. Moreover, the detection scales were increased to four based on the original YOLO-V3. The experiment on RSOD (Remote Sensing Object Detection) dataset and UCS-AOD (Dataset of Object Detection in Aerial Images) dataset showed that our approach performed better than Faster-RCNN, SSD (Single Shot Multibox Detector), YOLO-V3, and YOLO-V3 tiny in terms of accuracy. Compared with original YOLO-V3, the mAP (mean Average Precision) of our approach increased from 77.10% to 88.73% in the RSOD dataset. In particular, the mAP of detecting targets like aircrafts, which are mainly made up of small targets increased by 12.12%. In addition, the detection speed was not significantly reduced. Generally speaking, our approach achieved higher accuracy and gave considerations to real-time performance simultaneously for remote sensing target detection.
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