2000 character limit reached
What is YOLOv5: A deep look into the internal features of the popular object detector
Published 30 Jul 2024 in cs.CV | (2407.20892v1)
Abstract: This study presents a comprehensive analysis of the YOLOv5 object detection model, examining its architecture, training methodologies, and performance. Key components, including the Cross Stage Partial backbone and Path Aggregation-Network, are explored in detail. The paper reviews the model's performance across various metrics and hardware platforms. Additionally, the study discusses the transition from Darknet to PyTorch and its impact on model development. Overall, this research provides insights into YOLOv5's capabilities and its position within the broader landscape of object detection and why it is a popular choice for constrained edge deployment scenarios.
- A layer-wise surface deformation defect detection by convolutional neural networks in laser powder-bed fusion images. Materials, 15(20):7166, Oct. 2022.
- Muhammad Hussain. Yolov1 to v8: Unveiling each variant–a comprehensive review of yolo. IEEE Access, 12:42816–42833, 2024.
- Lightweight convolutional network for automated photovoltaic defect detection. In 2023 9th International Conference on Information Technology Trends (ITT), pages 133–138. IEEE, 2023.
- Muhammad Hussain. When, where, and which?: Navigating the intersection of computer vision and generative ai for strategic business integration. IEEE Access, 11:127202–127215, 2023.
- In-depth review of yolov1 to yolov10 variants for enhanced photovoltaic defect detection. In Solar, volume 4, pages 351–386. MDPI, 2024.
- You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 779–788, 2016.
- Muhammad Hussain. Yolo-v5 variant selection algorithm coupled with representative augmentations for modelling production-based variance in automated lightweight pallet racking inspection. Big Data and Cognitive Computing, 7(2):120, 2023.
- Ultralytics. Yolov5, 2024. Accessed: 2024-07-23.
- Glenn Jocher. ultralytics/yolov3: Yolov3 implementation in pytorch, 2024. Accessed: 2024-07-23.
- Cross-iteration batch normalization. In openaccess.thecvf.com, 2021.
- Roboflow Blog. Yolov4 vs. yolov5: Which one should you use?, 2023. Accessed: 2024-07-24.
- A comprehensive review of convolutional neural networks for defect detection in industrial applications. IEEE Access, 2024.
- Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934, 2020.
- PyTorch. Pytorch brand guidelines. Accessed: 2024-07-25.
- Roboflow Blog. What is an anchor box? https://blog.roboflow.com/what-is-an-anchor-box/. Accessed: 2024-07-25.
- Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4700–4708, 2017.
- Cspnet: A new backbone that can enhance learning capability of cnn. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pages 390–391, 2020.
- J. Solawetz. YOLOv5 New Version - Improvements and Evaluation. Roboflow Blog, Jun. 2020.
- Efficientdet: Scalable and efficient object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10781–10790, 2020.
- Brad Dwyer. Roboflow and ultralytics partner to streamline yolov5 mlops, 2021. Accessed: 2024-07-29.
- Deci AI. Deci ai documentation, 2024. Accessed: 2024-07-29.
- ClearML. Yolov5 clearml integration documentation, 2021. Accessed: 2024-07-29.
- Weights and Biases. Yolov5 weights and biases integration guide, 2021. Accessed: 2024-07-29.
- Deployment of ai-based rbf network for photovoltaics fault detection procedure. AIMS Electronics and Electrical Engineering, 4(1):1–18, 2019.
- A review on defect detection of electroluminescence-based photovoltaic cell surface images using computer vision. Energies, 16(10):4012, 2023.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.