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Jingxi Ma

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Proceedings Article | 6 November 2023 Paper

An object detection method based on YOLOv3 on infrared images

Mingming Zhu, Zongxin Liu, Yulei Zhao, Jun Li, Jingxi Ma

Proceedings Volume 12921, 1292148 (2023) https://doi.org/10.1117/12.2691760

KEYWORDS: Target detection, Object detection, Infrared radiation, Infrared imaging, Infrared detectors, Deep learning, Machine learning

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In order to improve the accuracy and speed of object detection on infrared images, an object detection method based on YOLOv3 is proposed. First, the rectangle filling, Mosaic data augmentation and adaptive anchors are used for data preprocessing, which lays the foundation for subsequent network training. Secondly, the convolution calculation layer and the cross stage partial module are used for the lightweight design to achieve high-speed detection while maintaining high-accurate. Then, spatial pyramid pooling is used to improve the learning ability of the network by enhancing the receptive field. Finally, bottom-up path augmentation is used to improve multi-layer feature fusion, which improves the transmission speed and utilization rate of low-level feature information. The experimental results show that the proposed method can detect cars accurately. Compared YOLOv3 methods, the proposed method has higher accuracy and faster speed, which meets the requirements of accurate and rapid detection on infrared images.

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