VE-YOLOv6: A Lightweight Small Target Detection Algorithm

At present, there are still many obstacles for small target detection in the field of computer vision. Although the existing target detection technology can extract the semantic information of small targets by multi-scale fusion and other methods, the detection speed is not ideal. In this paper, the...

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Bibliographic Details
Published in2024 4th International Conference on Neural Networks, Information and Communication (NNICE) pp. 873 - 876
Main Authors Wei, Jianguo, Qu, Yi, Gong, Meiheng, Ma, Yanbin, Zhang, Xin
Format Conference Proceeding
LanguageEnglish
Published IEEE 19.01.2024
Subjects
artificial intelligence
Artificial neural networks
Computational modeling
Computer architecture
Computer vision
deep learning
lightweight improvement
Object detection
Real-time systems
Semantics
small target detection
YOLOv6
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Summary:At present, there are still many obstacles for small target detection in the field of computer vision. Although the existing target detection technology can extract the semantic information of small targets by multi-scale fusion and other methods, the detection speed is not ideal. In this paper, the target detection algorithm of YOLOv6 is improved by lightweight. On the basis of VGNetG architecture, a lighter Backbone is redesigned, and ECA attention mechanism with extended position information is introduced to make up for some loss in detection accuracy, so as to ensure that the model can obtain lower computation without obvious decrease in detection accuracy. According to the test experiment of DOTA, a public data set, the FPS of VE-YOLOv6 proposed in this paper increases by 73, and the model computation and parameter number decrease by 18.91 and 2.8M, respectively, when the mAP decrease is not obvious with the original YOLOv6 algorithm.
DOI:10.1109/NNICE61279.2024.10498732