ArticleSensors (Basel, Switzerland)2022
SF-YOLOv5: A Lightweight Small Object Detection Algorithm Based on Improved Feature Fusion Mode.
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 21 papers.
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21 citing papers in PubMed.
- ADA-YOLO: An Adaptive Dynamic Aggregation Network for Small Object Detection in UAV Imagery.Sensors (Basel, Switzerland) · 2026Article
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- Enhancing object detection in remote sensing images with improved YOLOv8 model.Scientific reports · 2025Article
- An improved YOLO11 UAV toy models target detection model.Scientific reports · 2025Article
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- MSCD-YOLO: A Lightweight Dense Pedestrian Detection Model with Finer-Grained Feature Information Interaction.Sensors (Basel, Switzerland) · 2025Article
- Enabling scalable inspection of offshore mooring systems using cost-effective autonomous underwater drones.Frontiers in robotics and AI · 2025Article
- Development of an automated artificial intelligence-based system for urogenital schistosomiasis diagnosis using digital image analysis techniques and a robotized microscope.PLoS neglected tropical diseases · 2024Article
- High-quality expert annotations enhance artificial intelligence model accuracy for osteosarcoma X-ray diagnosis.Cancer science · 2024Article
- Assessing the Efficacy of the Spectrum-Aided Vision Enhancer (SAVE) to Detect Acral Lentiginous Melanoma, Melanoma In Situ, Nodular Melanoma, and Superficial Spreading Melanoma.Diagnostics (Basel, Switzerland) · 2024Article
- OD-YOLO: Robust Small Object Detection Model in Remote Sensing Image with a Novel Multi-Scale Feature Fusion.Sensors (Basel, Switzerland) · 2024Article
- An AI-based approach to create spatial inventory of safety-related architectural features for school buildings.Developments in the built environment · 2024Article
- Designing and development of agricultural rovers for vegetable harvesting and soil analysis.PloS one · 2024Article
- High-throughput platform for label-free sorting of 3D spheroids using deep learning.Frontiers in bioengineering and biotechnology · 2024Article
- Lightweight Network DCR-YOLO for Surface Defect Detection on Printed Circuit Boards.Sensors (Basel, Switzerland) · 2023Article
- Plant Parasitic Nematode Identification in Complex Samples with Deep Learning.Journal of nematology · 2023Article
- High-resolution processing and sigmoid fusion modules for efficient detection of small objects in an embedded system.Scientific reports · 2023Article
- Article
- An Efficient and Intelligent Detection Method for Fabric Defects based on Improved YOLOv5.Sensors (Basel, Switzerland) · 2022Article
- Development of YOLOv5-Based Real-Time Smart Monitoring System for Increasing Lab Safety Awareness in Educational Institutions.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the research of computer vision, a very challenging problem is the detection of small objects. The existing detection algorithms often focus on detecting full-scale objects, without making proprietary optimization for detecting small-size objects. For small objects dense scenes, not only the accuracy is low, but also there is a certain waste of computing resources. An improved detection algorithm was proposed for small objects based on YOLOv5. By reasonably clipping the feature map output of the large object detection layer, the computing resources required by the model were significantly reduced and the model becomes more lightweight. An improved feature fusion method (PB-FPN) for small object detection based on PANet and BiFPN was proposed, which effectively increased the detection ability for small object of the algorithm. By introducing the spatial pyramid pooling (SPP) in the backbone network into the feature fusion network and connecting with the model prediction head, the performance of the algorithm was effectively enhanced. The experiments demonstrated that the improved algorithm has very good results in detection accuracy and real-time ability. Compared with the classical YOLOv5, the mAP@0.5 and mAP@0.5:0.95 of SF-YOLOv5 were increased by 1.6% and 0.8%, respectively, the number of parameters of the network were reduced by 68.2%, computational resources (FLOPs) were reduced by 12.7%, and the inferring time of the mode was reduced by 6.9%.
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