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Huili Xin

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Proceedings Article | 23 August 2023 Paper

Lightweight small target detection algorithm based on YOLOv5

Nan Song, Ying Wang, Zhiyi Xin, Huili Xin, Tianxu Liu

Proceedings Volume 12784, 127843H (2023) https://doi.org/10.1117/12.2692005

KEYWORDS: Small targets, Target detection, Detection and tracking algorithms, Object detection, Convolution, Feature extraction

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To address the problems of current small target detection algorithms such as slow detection speed, high memory consumption, and not well applied to mobile devices, an improved lightweight small target detection algorithm MTG-YOLOv5 is proposed based on YOLOv5s algorithm. firstly, MobileNet V3 is used instead of the backbone network to reduce the number of network parameters and increase the speed of operation. Secondly, the small target detection layer is added to the original network structure to enhance the detection capability of small targets. Finally, Ghost module is introduced to replace the commonly used convolutional kernel to ensure detection accuracy and further light weighting. The improved algorithm was tested on the processed TinyPerson dataset. The experimental results show that the overall average accuracy of mAP of YOLOv5s algorithm is improved by 4.5%, the parameters are reduced by 1/2, the FPS is improved from 123.54 to 187.62, and the speed is enhanced by 51.87%. The algorithm effectively improves the detection speed while ensuring the detection accuracy.

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