Multi-Modal Object Detection Query Initialization
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Solution Overview
Problem
Existing object detection methods, particularly those using transformer architectures, face inefficiencies in initializing object queries, leading to suboptimal coverage of detection grids and potential misses when objects are densely arranged.
Innovation Solution
The proposed solution involves an apparatus and method for object detection using multi-modal sensor inputs, where initial object features are estimated for a large number of queries in a defined arrangement, and then the number of queries is reduced and their placement optimized based on statistical evaluations of merged feature correlations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large number of object queries are used to cover the entire detection grid, then object detection coverage is improved, but initialization overhead and computational complexity increase
Solution Approach 1:
The patent applies preliminary action by using sensor inputs (LIDAR and camera) to predict and initialize object query locations before the main detection process. This pre-positioning of queries at likely object locations based on multi-modal sensor fusion reduces the number of queries needed while maintaining detection coverage, thereby reducing initialization overhead without sacrificing reliability
Solution Approach 2:
The patent introduces multi-modal sensor inputs (LIDAR and camera features) as intermediaries to guide the initialization of object queries. These sensor features serve as mediators that provide spatial information about object locations, allowing the system to place queries more intelligently rather than using a fixed grid, thus reducing the total number of queries required while improving detection coverage
2Ease of manufacture
If object queries are initialized with LIDAR data only, then processing simplicity is maintained, but detection accuracy is reduced when camera features are available
Solution Approach 1:
The patent merges LIDAR and camera features through concatenation to create fused feature vectors that initialize object queries. This combination of multi-modal sensor data improves detection accuracy by utilizing complementary information from both sensors, while the merging process is designed to be computationally efficient, maintaining a reasonable level of processing simplicity
3Ease of operation
If a fixed distribution of object queries is used, then initialization simplicity is maintained, but objects in dense arrangements are missed
Solution Approach 1:
The patent implements dynamic initialization of object queries by using sensor-based predictions to adaptively determine query locations rather than using a static fixed grid. This dynamic approach allows the system to adjust query placement based on actual object locations detected by sensors, improving detection completeness in dense scenarios while maintaining operational simplicity through automated sensor-driven initialization
Data Source
AI summary
Object detection using multi-modal sensor input. The method includes: detecting first sensor data in an image section using a first sensor device and forming corresponding first feature vectors; detecting second sensor data in the image section using a second sensor device and forming corresponding second feature vectors; providing an arrangement including a grid with a predetermined first plurality of proposed locations for object search; extracting feature vectors from the first and second feature vectors, respective merging of first with second feature vectors at the proposed locations; generating a respective estimated bounding box for each of the proposed locations in the environment of the proposed locations, and calculating a respective confidence level each bounding box; reducing the first plurality of proposed locations to a second number of locations, based on the respective calculated confidence levels; and recognizing an object based on an object search at the second number of locations.