Rotated Object Detection With Cluster-Based Bounding Box Rotation
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Solution Overview
Problem
Existing object detection systems struggle with detecting rotated objects without requiring additional training data, leading to increased computational resources and annotation workload.
Innovation Solution
A method involving a neural network to determine bounding boxes, extract an initial bounding box, select a cluster center, circumscribe a cluster box, and rotate the initial bounding box based on the cluster box, using a trained machine learning model for detection, without retraining, and employing techniques like graph cut algorithms and non-maximum suppression to refine the bounding box.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing object detection systems are used to detect rotated objects, then the system can maintain its current training data and model structure, but the detection accuracy decreases and requires additional training data
Solution Approach 1:
The system performs preliminary actions by generating multiple candidate bounding boxes at various rotation angles before final detection. The neural network predicts bounding boxes at different rotations in advance, and the system selects the best matching box, avoiding the need to retrain with all possible rotation variations.
Solution Approach 2:
The system introduces dynamic rotation angles to the bounding box generation process. Instead of fixed-axis bounding boxes, the system dynamically adjusts rotation angles based on cluster analysis of candidate boxes, allowing the detection framework to adapt to rotated objects without retraining.
2Reliability
If additional training data with rotated objects is collected and annotated, then detection accuracy for rotated objects improves, but the annotation workload and computational resources increase
Solution Approach 1:
The system performs self-service by automatically generating rotated bounding box candidates and selecting the best match through cluster analysis. The neural network itself generates the rotation variations it needs for detection, eliminating the external need for manually annotated rotated training data.
Solution Approach 2:
The system changes parameters by introducing rotation angles as a variable parameter in the bounding box prediction. Instead of training on static images, the system modifies the bounding box parameters to include rotational components, allowing detection of rotated objects with the same training data.
3Reliability
If the machine learning model is retrained with additional rotated training data, then detection performance on rotated objects improves, but computational resources and training time increase
Solution Approach 1:
The system performs preliminary action by pre-computing cluster centers and rotation angles from candidate bounding boxes before final detection. This preliminary analysis allows the model to efficiently handle rotated objects during inference without having been explicitly trained on rotated examples.
Solution Approach 2:
The system applies partial action by generating bounding boxes at multiple rotation angles (excessive) but only selecting the best matching one (partial). This approach provides robustness against rotation without the full computational cost of training on all possible rotations.
4Measurement precision
If bounding boxes are refined through iterative processes like graph cut algorithms, then detection precision improves, but processing time increases
Solution Approach 1:
The system performs preliminary action by pre-generating candidate bounding boxes at multiple rotation angles and pre-computing cluster centers. This preliminary work reduces the iterative refinement steps needed later, balancing precision with processing efficiency.
Data Source
AI summary
At least one processor determines bounding boxes in an image using a neural network. At least one processor extracts an initial bounding box for an object in the image. At least one processor selects a cluster center of the bounding boxes and circumscribes a cluster box around the cluster center. At least one processor rotates the initial bounding box and contents of the initial bounding box according to a rotation angle determined based on the cluster box. At least one processor detects the object in the rotated initial bounding box using a trained machine learning model.