3D target detection method for point cloud screening based on image semantic features
A target detection algorithm and semantic feature technology, applied in image analysis, image enhancement, image data processing and other directions, can solve the problem of inability to achieve real-time detection, consume large computing resources, etc., to reduce the highly complex characteristics of input, real-time detection Good results
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[0037] The present invention provides a 3D target detection algorithm for point cloud screening based on image semantic features, and the specific process is shown in Figure 1.
[0038] Step (1): We use the current outstanding semantic segmentation method, DeepLabv3+, to segment the image. Since the image data of the 3D object detection dataset does not contain segmentation markers. We first manually label the image part of the training set in the dataset. We will pre-train DeepLabv3+ on the Cityscapes dataset for 200 epochs, and then fine-tune for 50 epochs on the hand-labeled semantic labels. The trained semantic segmentation network classifies each pixel in an image into one of 19 classes.
[0039] Step (2): Project the semantic prediction into the point cloud space, and select specific types of points to form the cone of view. The specific method is: based on the predicted results of the 2D semantic segmentation method, using the known projection matrix, the The area of...
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