Abstract
To improve the detection ability for small defects on the surface of the metal base of an infrared laser sensor, master the fluctuation and distribution of product quality, and form closed-loop control of production and quality improvement, the advanced You Only Look Once (YOLO) v5s (an improved YOLO model) object detection algorithm was further improved in this study. Specifically, the same-scale feature fusion part that is easily ignored in the structure was strengthened to enhance the network detection performance. In deep learning object detection, the propagation of information between features in the neural network is important. It was often represented by the pyramid features in the neck part of the model to enhance feature fusion. First, this study proposed a cross-convolution feature strengthening connection method combining the backbone and neck, which shortened the path of information propagation and improved the semantic information between feature pyramids. Then, the concat module of the original network was improved, and a new enhanced feature concat module was proposed to enhance the fusion of features at the same scale. The attention modules implemented by combining the convolutional block attention module were integrated into the concat module to enable the network to learn the weights of each channel independently, enhance the information transmission between features, and improve the detection performance of deep learning small objects. Lastly, the K-means + + algorithm was used to optimize the self-made Metal Base dataset of Infrared Laser Sensor (ILS-MB) and generate a new anchor box suitable for small objects in this dataset to improve the matching degree of target objects. With a small increase in computational cost, the improved YOLO v5s algorithm enhanced the accuracy by 3.8% on the ILS-MB dataset and achieved a very significant effect compared with other state-of-the-art detection methods.
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Data Availability
Declaration.
The ILS-MB dataset supporting the results of this study is used only under the license of the current study, so the data are not publicly available.
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Acknowledgements
This work was supported by the Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology (FMZ201901) and the National Natural Science Foundation of China “Research on bionic chewing robot for physical property detection and evaluation of food materials” (51375209).
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Zhu, X., Liu, J., Zhou, X. et al. Enhanced feature Fusion structure of YOLO v5 for detecting small defects on metal surfaces. Int. J. Mach. Learn. & Cyber. 14, 2041–2051 (2023). https://doi.org/10.1007/s13042-022-01744-y
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DOI: https://doi.org/10.1007/s13042-022-01744-y