ArticleScientific reports2022
A lightweight YOLOv3 algorithm used for safety helmet detection.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A cascaded detection method for railway foreign object intrusion in extreme weather based on improved LR-ASPP and fused Mamba-YOLO.Scientific reports · 2026Article
- A lightweight metallographic defect detection framework via structured priors and energy-aware attention under strong grain boundary interference.Scientific reports · 2026Article
- A method for detecting safety helmets underground based on the YOLOv11-SRA model.Scientific reports · 2026Article
- Deep vision in agriculture: assessing the function of YOLO in the classification of plant leaf diseases (PLDs).BioData mining · 2025Article
- Lightweight insulator target detection algorithm based on improved YOLOX.Scientific reports · 2025Article
- An improved YOLOv8 safety helmet wearing detection network.Scientific reports · 2024Article
- Morphology-based deep learning enables accurate detection of senescence in mesenchymal stem cell cultures.BMC biology · 2024Article
- A deep convolutional neural network for efficient microglia detection.Scientific reports · 2023Article
- Research on Safety Helmet Detection Algorithm Based on Improved YOLOv5s.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
YOLOv3 is a popular and effective object detection algorithm. However, YOLOv3 has a complex network, and floating point operations (FLOPs) and parameter sizes are large. Based on this, the paper designs a new YOLOv3 network and proposes a lightweight object detection algorithm. First, two excellent networks, the Cross Stage Partial Network (CSPNet) and GhostNet, are integrated to design a more efficient residual network, CSP-Ghost-Resnet. Second, combining CSPNet and Darknet53, this paper designs a new backbone network, the ML-Darknet, to realize the gradient diversion of the backbone network. Finally, we design a lightweight multiscale feature extraction network, the PAN-CSP-Network. The newly designed network is named mini and lightweight YOLOv3 (ML-YOLOv3). Based on the helmet dataset, the FLPSs and parameter sizes of ML-YOLOv3 are only 29.7% and 29.4% of those of YOLOv3. Compared with YOLO5, ML-YOLOv3 also exhibits obvious advantages in calculation cost and detection effect.
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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.