In recent years, the excellent image-based object detection algorithms are transferred to the video object detection directly. These frame-by-frame processing methods are suboptimal owing to the degenerate object appearance such as motion blur, defocus and rare poses. The existing works for video object detection mostly focus on the feature aggregation at pixel level and instance level, but the blur impact in the aggregation process has not been exploited well so far. In this paper, we propose an end-to-end blur-aid feature aggregation network (BFAN) for video object detection. The proposed BFAN focuses on the aggregation process influenced by the blur including motion blur and defocus with high accuracy and little increased computation. In BFAN, we evaluate the object blur degree of each frame as the weight for aggregation. Noteworthy, the background is usually flat which has a negative impact on the object blur degree evaluation. Therefore, we introduce a light saliency detection network to alleviate the background interference. The experiments conducted on the ImageNet VID dataset show that BFAN achieves the stateof-the-art detection performance, exactly 79.1% mAP, with 3 points improvement compared to the video object detection baseline. INDEX TERMS Video object detection, object blur degree evaluation, saliency detection.
Background
For the encoding part of U-Net3+,the ability of brain tumor feature extraction is insufficient, as a result, the features can not be fused well during up-sampling, and the accuracy of segmentation will reduce.
Methods
In this study, we put forward an improved U-Net3+ segmentation network based on stage residual. In the encoder part, the encoder based on the stage residual structure is used to solve the vanishing gradient problem caused by the increasing in network depth, and enhances the feature extraction ability of the encoder which is instrumental in full feature fusion when up-sampling in the network. What’s more, we replaced batch normalization (BN) layer with filter response normalization (FRN) layer to eliminate batch size impact on the network. Based on the improved U-Net3+ two-dimensional (2D) model with stage residual, IResUnet3+ three-dimensional (3D) model is constructed. We propose appropriate methods to deal with 3D data, which achieve accurate segmentation of the 3D network.
Results
The experimental results showed that: the sensitivity of WT, TC, and ET increased by 1.34%, 4.6%, and 8.44%, respectively. And the Dice coefficients of ET and WT were further increased by 3.43% and 1.03%, respectively. To facilitate further research, source code can be found at: https://github.com/YuOnlyLookOne/IResUnet3Plus.
Conclusion
The improved network has a significant improvement in the segmentation task of the brain tumor BraTS2018 dataset, compared with the classical networks u-net, v-net, resunet and u-net3+, the proposed network has smaller parameters and significantly improved accuracy.
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