Object-based loss function in segmented neural networks

Jin Liu; Qun Li

doi:10.1117/12.2541908

14 February 2020 Object-based loss function in segmented neural networks

Jin Liu, Qun Li

Proceedings Volume 11432, MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 114320N (2020) https://doi.org/10.1117/12.2541908
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

Abstract

This paper proposes Object-based Loss Function in Segmented Neural Networks. Traditional Segmented Neural Network(SNN) are based on Pixel-based Back Propagation(PBP). Since the pixel ratios of the images occupied by different sizes of objects are not the same, the weight of the small objects in the segmentation is small, which means using PBP may greatly affects the accuracy of the detection when there are a large number of small objects. Considering this defect of PBP, we propose a Object-based Back Propagation(OBP) loss function weight design, that is, the back propagation weights of different objects are not equal, which is inversely proportional to the area occupied by the object. Segmented Neural Networks data set test.

Citation Download Citation

Jin Liu and Qun Li "Object-based loss function in segmented neural networks", Proc. SPIE 11432, MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 114320N (14 February 2020); https://doi.org/10.1117/12.2541908

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