A lightweight real-time infrared image instance segmentation algorithm based on tiny YOLO

Jing Nan; Lei Bo

doi:10.1117/12.2586889

12 March 2021 A lightweight real-time infrared image instance segmentation algorithm based on tiny YOLO

Jing Nan, Lei Bo

Proceedings Volume 11763, Seventh Symposium on Novel Photoelectronic Detection Technology and Applications; 1176341 (2021) https://doi.org/10.1117/12.2586889
Event: Seventh Symposium on Novel Photoelectronic Detection Technology and Application 2020, 2020, Kunming, China

Abstract

A lightweight real-time infrared image instance segmentation algorithm model is proposed. Experiments are carried out on the infrared image data set. Aiming at solving the problem that the segmentation accuracy of the model is not high, the following two improvements are made: firstly, the improved tiny Yolo backbone network is added with feature pyramid network to refine the features, highlighting the boundary and small object features; Then, aiming at the problem that large objects account for more in the loss function, three different sizes of anchor, prediction head and prototype mask networks are introduced, which are connected to the segmentation model for end-to-end training, so that small objects can make greater contributions to the network. Experiments show that the lightweight real-time infrared image segmentation model achieves higher segmentation accuracy for small objects.

Citation Download Citation

Jing Nan and Lei Bo "A lightweight real-time infrared image instance segmentation algorithm based on tiny YOLO", Proc. SPIE 11763, Seventh Symposium on Novel Photoelectronic Detection Technology and Applications, 1176341 (12 March 2021); https://doi.org/10.1117/12.2586889

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