Abstract
Deep Learning algorithms based on convolutional neural network (CNN) have achieved huge success in semantic segmentation. However, in these networks, sub-sampling will cause a large loss of image detailed information. In this work, we design a novel method for recovering some of the lost pixels. We use two-dimensional discrete Wavelet Transform (DWT) to extract image boundary detailed information and combine the segmentation result of the convolutional network to recover some of the lost details. We analyze the influence of the algorithm parameters and wavelet function on the final prediction. In our experiments, our algorithm has accuracy improvement compared to the deep network on the PASCAL VOC 2012 dataset.
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Acknowledgement
This work was supported by Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission.
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Yin, X., Xu, X. (2022). A Method for Improving Accuracy of DeepLabv3+ Semantic Segmentation Model Based on Wavelet Transform. In: Liang, Q., Wang, W., Liu, X., Na, Z., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2021. Lecture Notes in Electrical Engineering, vol 879. Springer, Singapore. https://doi.org/10.1007/978-981-19-0386-1_39
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DOI: https://doi.org/10.1007/978-981-19-0386-1_39
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