ArticleSensors (Basel, Switzerland)2024
A Lightweight Remote Sensing Small Target Image Detection Algorithm Based on Improved YOLOv8.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- A Lightweight End-to-End Framework for Real-Time Vehicle-Ejected Debris Detection on Edge Devices.Sensors (Basel, Switzerland) · 2026Article
- AL-YOLOv8: A Small Object Detection Algorithm for Remote Sensing Images Based on an Improved YOLOv8s.Sensors (Basel, Switzerland) · 2026Article
- Research on target detection algorithm for forest fire images based on multi-scale feature extraction.Scientific reports · 2026Article
- Lightweight target detection and multi target tracking for UAV inspection in open pit mines.Scientific reports · 2026Article
- MSFN-YOLOv11: A Novel Multi-Scale Feature Fusion Recognition Model Based on Improved YOLOv11 for Real-Time Monitoring of Birds in Wetland Ecosystems.Animals : an open access journal from MDPI · 2025Article
- Object detection model of vehicle-road cooperative autonomous driving based on improved YOLO11 algorithm.Scientific reports · 2025Article
- LEAD-YOLO: A Lightweight and Accurate Network for Small Object Detection in Autonomous Driving.Sensors (Basel, Switzerland) · 2025Article
- Automated Discrimination of Appearance Quality Grade of Mushroom (Sensors (Basel, Switzerland) · 2025Article
- HSF-YOLO: A Multi-Scale and Gradient-Aware Network for Small Object Detection in Remote Sensing Images.Sensors (Basel, Switzerland) · 2025Article
- Detection of Crack Sealant in the Pretreatment Process of Hot In-Place Recycling of Asphalt Pavement via Deep Learning Method.Sensors (Basel, Switzerland) · 2025Article
- Tri-band vehicle and vessel dataset for artificial intelligence research.Scientific data · 2025Article
- ARSOD-YOLO: Enhancing Small Target Detection for Remote Sensing Images.Sensors (Basel, Switzerland) · 2024Article
- An Improved Bird Detection Method Using Surveillance Videos from Poyang Lake Based on YOLOv8.Animals : an open access journal from MDPI · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
In response to the challenges posed by small objects in remote sensing images, such as low resolution, complex backgrounds, and severe occlusions, this paper proposes a lightweight improved model based on YOLOv8n. During the detection of small objects, the feature fusion part of the YOLOv8n algorithm retrieves relatively fewer features of small objects from the backbone network compared to large objects, resulting in low detection accuracy for small objects. To address this issue, firstly, this paper adds a dedicated small object detection layer in the feature fusion network to better integrate the features of small objects into the feature fusion part of the model. Secondly, the SSFF module is introduced to facilitate multi-scale feature fusion, enabling the model to capture more gradient paths and further improve accuracy while reducing model parameters. Finally, the HPANet structure is proposed, replacing the Path Aggregation Network with HPANet. Compared to the original YOLOv8n algorithm, the recognition accuracy of mAP@0.5 on the VisDrone data set and the AI-TOD data set has increased by 14.3% and 17.9%, respectively, while the recognition accuracy of mAP@0.5:0.95 has increased by 17.1% and 19.8%, respectively. The proposed method reduces the parameter count by 33% and the model size by 31.7% compared to the original model. Experimental results demonstrate that the proposed method can quickly and accurately identify small objects in complex backgrounds.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.