The multimodal target detection algorithm has the problem of poor feature fusion ability of different modes, which leads to poor detection accuracy. Therefore, this paper improves and optimizes the MVX-Net algorithm, and proposes an adaptive multi-modal feature fusion algorithm AF-MVX-Net (adaptive fusion). The algorithm is based on the MVX-Net framework, and an adaptive multi-modal feature Fusion module AFM (Adaptation Fusion Module) is added. The module was designed by analyzing the relationship between local and global features to adaptively enhance the weighting of important features in the fused data to improve the effectiveness of multimodal fusion, thus improving detection accuracy. The results of the experimental verification on the KITTI dataset demonstrate that the average 3DAP value of all categories of simple targets has increased by 8.55% to 76.1%. ; For vehicle categories, the value of 3DAP@0.7 increased by 2%; Bicycle category 3DAP@0.5 value increased by 5~6%; The 3DAP@0.5 value of the pedestrian category increased by 10~13%, which effectively improves the detection accuracy of bicycles, pedestrians and vehicles in the automatic driving scenario, so FA-MVX-Net algorithm is proved to be effective.
Due to the characteristics of multi-scale and complex edge shape of X-ray film weld defects, the segmentation accuracy of conventional segmentation methods is low. Aiming at the characteristics of these defects, this paper proposes an X-ray weld defect segmentation method based on deep separable convolution structure. Firstly, a multi-channel information fusion module is innovatively designed to replace the two consecutive 3×3 convolutions. Secondly, a spatial depth separation attention mechanism is added after the skip connection. Finally, a tail feature information cascade module is proposed to fuse different levels of feature information. This method can show high performance when segmenting X-ray film weld defects with multi-scale and complex edge shapes. The experimental results on the public dataset GDX-ray show that the proposed method is 6.1 % and 6.4 % higher than the classical Unet method in DSC value and precision P respectively, which effectively improves the segmentation accuracy of X-ray weld defects.
In order to improve the accuracy and robustness of correlation filtering algorithm in more challenging scenarios, a target tracking algorithm based on adaptive channel sample weights is proposed in this paper. Firstly, channel attention modules are added to sample branch and search branch respectively to weight the features of each branch respectively, which can effectively improve the feature expression ability. Secondly, according to the channel weights of the two branches, we learn to fuse the weight information, interact the features of the two branches, and suppress the background information. Finally, the sample learning weights are reassigned according to the channel weights of the history frame to make more efficient use of the background information. In this paper, the OTB100 data set is used to verify the effectiveness of the improved algorithm. The area under the curve is increased by 1.6%, which proves the effectiveness of the improved algorithm.
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