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基于改进YOLOv5的路面病害检测模型 - 中国知网
Source: kns.cnki.net
Article summary:
1. This article proposes a new model, YOLOv5l-CBF, based on the convolutional neural network YOLOv5 for road surface disease detection.
2. The model introduces a coordinate attention mechanism to adjust the network's attention weights and improve its recognition and classification accuracy.
3. It also incorporates Transformer into the residual structure of the backbone network and builds a BotNet network structure to capture global dependencies in disease images while reducing parameter quantity.
Article analysis:
The article is generally reliable and trustworthy as it provides detailed information about the proposed model, YOLOv5l-CBF, which is based on the convolutional neural network YOLOv5 for road surface disease detection. The article explains how this model introduces a coordinate attention mechanism to adjust the network's attention weights and improve its recognition and classification accuracy, as well as how it incorporates Transformer into the residual structure of the backbone network and builds a BotNet network structure to capture global dependencies in disease images while reducing parameter quantity. Furthermore, it provides experimental results from real road surface disease datasets that demonstrate significant performance advantages in detecting and classifying various diseases compared to YOLOv5l models.
There are no potential biases or one-sided reporting present in this article as it presents both sides equally by providing an overview of both existing models (YOLOv5l) and proposed models (YOLOv5l-CBF). Additionally, all claims made are supported with evidence from experiments conducted on real road surface disease datasets. There are no missing points of consideration or missing evidence for any claims made either. All counterarguments have been explored thoroughly throughout the article as well as promotional content being absent from it. Lastly, possible risks have been noted throughout the article such as noise interference due to diverse forms of diseases, numerous types of diseases, and similar background gray values which can affect recognition accuracy negatively.