Scene-Aware Object Detection via Region Segmentation
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Solution Overview
Problem
Current object detection methods are inefficient in handling pose-diverse categories due to high computational demands and suboptimal performance, especially when dealing with complex categories that require large training datasets and extensive computational resources.
Innovation Solution
The system employs a neural network-based object detector that modulates its operations based on the location of sensor data, using feature extractors and filters that parameterize transformation operations, and implements unsupervised pruning of filters with low activations across layers, allowing for efficient detection of objects in sensor data by adapting to specific viewpoints and locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-layer deep Convolutional Neural Networks are used to detect pose-diverse categories, then detection accuracy is improved, but computational time increases significantly
Solution Approach 1:
The patent segments the image into multiple regions of interest based on detected keypoints (such as corners, edges, or semantic landmarks). Instead of applying the heavy multi-layer CNN to the entire image, the network is applied only to these segmented regions, significantly reducing computational time while maintaining detection accuracy for pose-diverse objects.
Solution Approach 2:
The patent performs preliminary keypoint detection and region segmentation before applying the multi-layer CNN. This preliminary action identifies and isolates the most relevant areas containing pose-diverse objects, allowing the computationally intensive network to focus only on these regions rather than processing the entire image, thus reducing overall computational time.
2Reliability
If large training datasets with hundreds of thousands of samples are used, then detection reliability for pose-diverse categories is improved, but training complexity and resource requirements increase
Solution Approach 1:
The patent applies local quality by focusing training efforts on specific regions of interest rather than uniformly processing entire images. By identifying and training on segmented regions containing pose-diverse objects, the system achieves reliable detection with fewer training samples, reducing training complexity and resource requirements while maintaining high detection reliability.
Solution Approach 2:
The patent performs preliminary region segmentation and keypoint identification during training, allowing the system to pre-identify relevant training regions. This preliminary action enables more efficient use of training data by focusing computational resources on the most informative regions, reducing the overall training complexity and resource requirements.
3Measurement precision
If pose-partitioning is applied to training data, then detection precision for specific poses is improved, but the granularity requires sufficient training data in each partition
Solution Approach 1:
The patent segments images into regions based on detected keypoints and object locations, then applies pose-specific detection only to these segmented regions. This approach maintains pose-specific detection precision while avoiding the need to partition the entire training dataset, as the segmentation process naturally isolates relevant regions regardless of pose variations.
Solution Approach 2:
The patent creates a universal detection framework that handles multiple poses within a single training process. By using keypoint-based region segmentation that adapts to different poses, the system achieves pose-specific precision without requiring separate training partitions for each pose, thus eliminating the data availability constraint associated with fine-grained pose partitioning.
Data Source
AI summary
Embodiments described herein provide systems and processes for scene-aware object detection. This can involve an object detector that modulates its operations based on image location. The object detector can be a neural network detector or a scanning window detector, for example.