3D Object Detection Using Semantic Segmentation to Filter False Positives
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Solution Overview
Problem
3D object detection models in autonomous driving systems suffer from high false positive rates due to sparse data representation, occlusion, and adverse environmental conditions, leading to safety risks and reduced system reliability.
Innovation Solution
The techniques utilize camera data and LiDAR point clouds to generate 3D bounding boxes and semantic segmentation, employing box-level statistical features, threshold comparisons, and machine learning models like multi-layer perceptrons, or graph convolutional networks to distinguish between true and false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If 3D object detection models use sparse data representation from cameras and LiDAR, then the system can operate in diverse environmental conditions, but the false positive rate increases due to insufficient data density
Solution Approach 1:
The patent combines camera images and LiDAR point clouds into a unified 3D detection framework. The camera data provides rich texture and color information while LiDAR provides accurate depth and spatial structure. By merging these complementary data sources through multi-view geometry and point cloud registration, the system achieves robust object detection that maintains low false positive rates even in sparse data conditions, resolving the contradiction between environmental adaptability and detection reliability
Solution Approach 2:
The patent introduces 3D semantic segmentation as an intermediary process between raw sensor data and object detection. The segmentation module first partitions the 3D space into semantically meaningful regions (road, sidewalk, vegetation, etc.), which then serves as contextual prior knowledge for the detection model. This intermediary step helps filter false positives by ensuring detected objects are located in semantically appropriate regions, thereby maintaining reliability across diverse environmental conditions
2Measurement precision
If the detection model processes more data to reduce false positives, then detection accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the detection process into distinct modular stages: data acquisition, preprocessing, 3D semantic segmentation, feature extraction, and object detection. Each stage processes only the necessary data for its specific function, avoiding redundant computations. The semantic segmentation module processes the entire 3D space once to create a contextual map, which then guides subsequent detection operations, reducing overall computational complexity while maintaining high detection accuracy
Solution Approach 2:
The patent performs 3D semantic segmentation as a preliminary action before object detection. By pre-processing the scene to identify and label different semantic regions (road surfaces, sidewalks, vegetation, buildings), the system creates a contextual framework that constrains and guides the subsequent object detection process. This preliminary action reduces the search space and eliminates many potential false positives before the main detection algorithm runs, thereby reducing computational complexity while improving accuracy
3Reliability
If the system uses advanced processing techniques like graph convolutional networks, then false positive reduction improves, but the ease of operation and implementation difficulty worsens
Solution Approach 1:
The patent introduces 3D semantic segmentation as an intermediary that simplifies the detection task for advanced models. By pre-processing the scene to create semantically labeled 3D regions, the segmentation module transforms the complex raw sensor data into a structured representation that is easier for graph convolutional networks to process. This intermediary step reduces the implementation difficulty by providing a standardized input format and reducing the model's burden of learning basic scene understanding from scratch
Data Source
AI summary
An apparatus for object detection includes memory and processing circuitry configured to obtain camera data and depth data representing a scene. The processing circuitry generates 3D bounding boxes for one or more objects in the scene based on the camera data and generates a 3D semantic segmentation from the depth data. Using the 3D bounding boxes and the 3D semantic segmentation, the processing circuitry calculates box statistics to determine which 3D bounding boxes correspond to true positive objects and which correspond to false positive objects. Final 3D bounding boxes for the true positive objects are then output.