In order to solve the problems of missing detection and poor detection effect of small targets in autonomous driving scenarios, a road target detection algorithm with improved YOLOv8 algorithm was proposed. Firstly, the backbone network is replaced by FasterNet, which combines the multi-scale attention mechanism and depth separable convolution to improve the feature expression and receptive field range. Secondly, CBAM is integrated into the attention mechanism module, which combines the channel attention mechanism with the spatial attention mechanism to form a new convolutional block structure, so as to better carry out feature fusion. Finally, to solve the problem that CIOU loss function does not take into account the mismatch between the desired real frame and the predicted frame, Inner-SIoU loss function is introduced to effectively improve the accuracy of reasoning. Experimental results show that for the public Udacity data set, the proposed algorithm can improve the detection accuracy by2.9% while maintaining the same detection speed as the original algorithm.
With the rapid development of computer vision technology, the application field of target detection is becoming higher and higher. With the continuous upgrading of UAV technology, the acquisition of remote sensing images has become more and more simple, the spatial resolution of remote sensing images has become higher and higher, and the information of images has become more abundant. There are many small targets in remote sensing images, the target size is quite different, and the background information is complex. In view of these problems, this paper proposes an improved YOLOv5 remote sensing image target detection algorithm based on YOLOv5 algorithm. Firstly, the backbone network of YOLOv5 is replaced by Swin Transformer, and the hierarchical feature map is constructed by using the displacement window, which effectively adapts to the computer vision task. Secondly, the SA (Shuffle Attention) attention mechanism is added to the network. The experimental results show that for the public DIOR dataset, the improved algorithm improves the detection accuracy by 1.7 % while maintaining the same detection speed as the original algorithm.
In view of the strong subjectivity of traditional salt dome recognition methods and the poor effect of existing deep learning algorithms on salt dome edge recognition, this paper proposes a salt dome recognition algorithm based on reverse attention mechanism, which uses u-net model as the backbone network, adds reverse attention module at the jump connection to extract edge structure information, and finally uses feature splicing to fuse feature information to improve the segmentation performance of network model. Experimental results show that the network achieves good results in salt dome segmentation, and effectively improves the problem of unclear edge segmentation.
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