Light detection algorithm of pyrotechnics based on improved YOLOv5s

Home > Archive>Volume 47, Issue 17, 2024 >140-146

Light detection algorithm of pyrotechnics based on improved YOLOv5s
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                        Zhao SonghuaiZhao Songhuai
1.Key Laboratory of Metallurgical Equipment and Its Control, Ministry of Education, Wuhan University of Science and Technology， Wuhan 430081, China； 2.Hubei Key Laboratory of Mechanical Transmission and Control Engineering, Wuhan University of Science and Technology，Wuhan 430081, China
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Zhou MinZhou Min
1.Key Laboratory of Metallurgical Equipment and Its Control, Ministry of Education, Wuhan University of Science and Technology， Wuhan 430081, China； 2.Hubei Key Laboratory of Mechanical Transmission and Control Engineering, Wuhan University of Science and Technology，Wuhan 430081, China
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Shen FeiShen Fei
1.Key Laboratory of Metallurgical Equipment and Its Control, Ministry of Education, Wuhan University of Science and Technology， Wuhan 430081, China； 2.Hubei Key Laboratory of Mechanical Transmission and Control Engineering, Wuhan University of Science and Technology，Wuhan 430081, China
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Xiang FengXiang Feng
1.Key Laboratory of Metallurgical Equipment and Its Control, Ministry of Education, Wuhan University of Science and Technology， Wuhan 430081, China； 2.Hubei Key Laboratory of Mechanical Transmission and Control Engineering, Wuhan University of Science and Technology，Wuhan 430081, China
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Affiliation:1.Key Laboratory of Metallurgical Equipment and Its Control, Ministry of Education, Wuhan University of Science and Technology， Wuhan 430081, China; 2.Hubei Key Laboratory of Mechanical Transmission and Control Engineering, Wuhan University of Science and Technology，Wuhan 430081, China
Clc Number:TP391；TN911.73
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Abstract:

Aiming at the problems of the traditional sensor′s late detection of fireworks and its inability to give details of fireworks, as well as the imbalance between detection efficiency and accuracy of the current mainstream fireworks detection algorithms, an improved YOLOv5s light detection algorithm for fireworks was proposed. The second convolutional module in Backbone is replaced with Stem module, which can improve the model′s detection performance of small target space information and effectively control the total floating point operand. C3Ghost module and Ghost convolution module are introduced in Backbone and Neck to reduce the number of network parameters and improve the performance of fireworks detection. In order to distinguish the importance of different features in the process of feature fusion, a structure of adding learnable weight parameters to PAN is proposed, which significantly improves the average accuracy of fireworks detection. The experimental results show that compared with the original model, the weight of the model is reduced from 14.4 M to 10.2 M, GFLOPs is reduced from 15.8 to 3.7, and the average accuracy is increased by 1.1%. The improved model has improved the performance of pyrotechnic detection while being lightweight.

Key words:pyrotechnic detection;YOLOv5s;feature fusion;lightweight algorithm;small target detection

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Online: December 11,2024
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