论文写作篇#4：YOLO还能发C会论文吗？C会论文的YOLO文章结构解析
本文分析了YOLO作为主流目标检测模型在不同垂直领域的改进，展示了多篇关于YOLO改进的会议论文，篇幅一般控制在9-13页，结构灵活，尽管没有统一的格式要求。文章总结了8篇论文的结构和参考文献情况，C会论文参考文献主要引用了其他会议论文、期刊论文和arXiv预印本，没有死板要求。
先说结论：能发，YOLO作为近年来发展迅速的主流目标检测模型，具有相当强的可拓展性。目前仍有大量的会议接受关于改进YOLO的文章，基本的改进模型主要集中在YOLOv5、YOLOv7、YOLOv8、YOLOv10这几个之中。
C会文章结构解析：
篇幅：
我找到了一个CCF的C会，在其会议论文集中挑取了8篇关于YOLO的文章，其内容都是关于YOLO在某个垂直领域的改进。
我们可以看到会议论文的篇幅整体控制在9-13页左右，包含了参考文献和附录。这主要是因为会议论文要求的语言精简和超页付费的机制。 大家在投会议的时候，一定要去目标会议官网下载模板以及看清作者指导、价格等等因素。
结构（章节分布）：
以下是我总结的8篇文章的标题、篇幅和分段情况：
| 序号 | 标题 | 篇幅 | 分段 |
| 1 | AYOLOv8: Improved Detector Based on YOLOv8 to Focus More on Small and Medium Objects | 13 | 1.Introcuction(包含本文改进) 2.Methods(一段总起，分点论述) 3.Results and Anlysis(总起一句数据集， Implementation Details，消融实验、对比结果可视化分三点) 4.Conclusions |
| 2 | YOLO-Fire: A Fire Detection Algorithm Based on YOLO | 13 | 1.Introcuction(包含本文改进) 2.YOLO-Fire(总起概述改进后模型，后分点介绍) 3.Experiments(Experiments, Ablation Studies, Experimental Results Analysis分三点) 4.Conclusions |
| 3 | DCM-YOLOv8: An Improved YOLOv8-Based Small Target Detection Model for UAV Images | 13 | 1.Introcuction(包含本文改进) 2.Related Work(YOLOv8,金字塔池化、空间金字塔三点) 3.Proposed Method(分三点介绍) 4.Experiments and Results(Dataset and Implementation Details, Evaluation Metrics, Ablation Experiment, Comparison Experiments) 5.Conclusion |
| 4 | Improved YOLOv8-Based Lightweight Object Detection on Drone Images | 9 | 1.Introcuction(不包含本文改进) 2.The Proposed Algorithm(总算一点，共五点) 3.Experimental Results and Analysis (Experimental Results and Analysis,Ablation Study, Comparative Experiments Pruning Experiments ) 4.Conclusion |
| 5 | A YOLOv7-Based Defect Detection Method for Metal Surfaces | 11 | 1.Introcuction(不包含本文改进) 2.Related Studies(YOLO史) 3. DA-YOLOv7 Model( DA-YOLOv7 Model, SPPCBAMC Structure, MPDIou Loss Function) 4. Experimental Results(Dataset and Experimental Environment , Data Preprocessing, Evaluation Indicators, Ablation Study, Effect Analysis对比实验) 5.Conclusion |
| 6 | Improved YOLOv7-Tiny Insulator Defect Detection Based on Drone Images | 9 | 1.Introcuction(不包含本文改进) 2.Related Work( Model Design, Lightweight ELAN-DW Module, CBAM， Loss Function Improvement四点)实际上就是改进措施 3.Experiment(Experiment, Experimental Environment, Evaluation Indicators) 4.Experimental Results and Analysis(Ablation Experiment, Target Detection Algorithm Comparison Experiment) 5.Conclusion |
| 7 | Pest-YOLO: A Lightweight Pest Detection Model Based on Multi-level Feature Fusion | 12 | 1.Introcuction(包含本文改进) 2.Data Augmentation 3.Method(三点) 4.Experimental Analysis(Experimental Dataset, Experimental Environment, Comprehensive Experimental Results(Performance Comparison of ClassicalModels.;Ablation Experiments.)) 5.Conclusion |
| 8 | DYOLO: A Novel Object Detection Model for Multi-scene and Multi-object Based on an Improved D-Net Split Task Model is Proposed | 11 | 1.Introcuction(包含大量本文改进描写) 2.Related Work(极短，一段话) 3.Models and Methods(YOLOv8和改进后的都画了结构图, 实际只有DConv一点) 4.Experiments and Analysis(Datasets, Experimental Environment and Evaluation Indicators, Comparative Analysis of Ablation Experiments, Performance Comparison of Improved Algorithms and Existing Object Detection Algorithms) 5.Conclusion |
可以看到，会议C会对YOLO文章的结构没有一个标准的模板，这也符合了会议所想达到的互相交流而不被拘泥于文章结构上的目标。大家可以根据需要参照一篇适合自己已有基础的文章进行参考。
参考文献：
参考文献也是充满讲究的一个部分，引用了哪些参考文献也存在一定的规律。
| 序号 | 标题 | 参考会议文章数 | 参考文献数 |
| 1 | AYOLOv8: Improved Detector Based on YOLOv8 to Focus More on Small and Medium Objects | 14(50%)还参考许多预印本，期刊较少 | 28 |
| 2 | YOLO-Fire: A Fire Detection Algorithm Based on YOLO | 7(46.67%)还参考许多预印本，期刊较少 | 15 |
| 3 | DCM-YOLOv8: An Improved YOLOv8-Based Small Target Detection Model for UAV Images | 7(38.89%)还参考少量预印本，期刊较多 | 18 |
| 4 | Improved YOLOv8-Based Lightweight Object Detection on Drone Images | 6(37.5%)还参考大量预印本，一些期刊 | 16 |
| 5 | A YOLOv7-Based Defect Detection Method for Metal Surfaces | 2(11.76%)以预印本和期刊为主 | 17 |
| 6 | Improved YOLOv7-Tiny Insulator Defect Detection Based on Drone Images | 3(15%)以期刊为主，其次预印本 | 20 |
| 7 | Pest-YOLO: A Lightweight Pest Detection Model Based on Multi-level Feature Fusion | 10(43.48%)其次期刊再次预印本 | 23 |
| 8 | DYOLO: A Novel Object Detection Model for Multi-scene and Multi-object Based on an Improved D-Net Split Task Model is Proposed | 0(0%)以期刊为主，其次预印本 | 18 |
以上是我整理的每篇论文的参考文献数量，以及参考会议论文的数量与比例。我们可以看到，会议论文占参考文献的占比在0-50%之间，没有一个很稳定的范围。这也可以说明C会论文对参考文献没有特别死板的要求。但我们奇妙地发现，每篇文章都引用了arXiv预印本的论文。arXiv预印本的详细介绍大家可以参考下面这篇文章：
总结：
本文分析了YOLO作为主流目标检测模型在不同垂直领域的改进，展示了多篇关于YOLO改进的会议论文，篇幅一般控制在9-13页，结构灵活，尽管没有统一的格式要求。文章总结了8篇论文的结构和参考文献情况，C会论文参考文献主要引用了其他会议论文、期刊论文和arXiv预印本，没有死板要求。
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