Dataset-Driven Anchor Generation for Multi-Scale Object Detection
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Solution Overview
Problem
Current multi-scale object detection methods in computer vision face challenges in achieving accurate object detection across varying object sizes and proportions without overloading system resources, often relying on public datasets that may not match the characteristics of the training dataset, leading to performance losses and inefficiencies.
Innovation Solution
A method for generating anchors and selecting feature maps based on the analysis of the training dataset, which calculates scale and ratio distributions of ground-truth objects to tailor anchor scales and ratios, ensuring accurate object detection without excessive resource usage, applicable to both one-stage and two-stage object detectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a fixed number of anchors is used for all datasets, then the object detection method is simple to implement, but the detection accuracy decreases when the dataset characteristics differ from the training dataset
Solution Approach 1:
The patent implements dynamic anchor generation by analyzing the scale distribution of ground-truth objects in the dataset and automatically determining the number of anchors based on the effective scale range. This transforms the static anchor configuration into a dynamic process that adapts to different dataset characteristics, resolving the contradiction between implementation simplicity and detection accuracy.
Solution Approach 2:
The patent changes the parameter of anchor number from a fixed value to a variable determined by dataset analysis. By calculating the scale distribution and using the effective scale range to determine the number of anchors, the system adapts parameters based on actual data characteristics, improving accuracy without requiring manual tuning for each dataset.
2Measurement precision
If the number of anchors is increased to improve detection accuracy across diverse object scales, then detection accuracy improves, but system resource usage increases
Solution Approach 1:
The patent applies partial action by generating only the necessary number of anchors based on the effective scale range rather than using a large fixed number of anchors for all cases. This generates sufficient anchors to cover the actual object scale distribution without the excessive resource consumption that would result from using a universally large anchor set.
Solution Approach 2:
The patent dynamically adjusts the anchor number parameter based on the analyzed scale distribution, changing it from a static high-value setting to an adaptive value that matches the dataset requirements. This parameter change optimizes the balance between detection accuracy and resource efficiency.
3Adaptability or versatility
If public dataset standards are used for anchor generation, then the method is universally applicable, but performance is lost when dataset characteristics differ from public datasets
Solution Approach 1:
The patent implements self-service by enabling the system to automatically analyze its own input dataset and generate appropriate anchor configurations without relying on external public dataset standards. The system serves itself by determining the effective scale range and anchor number based on the actual data it processes, eliminating the performance loss associated with using mismatched public dataset parameters.
Solution Approach 2:
The patent changes the anchor generation parameters from fixed public dataset standards to dataset-specific values determined through automatic analysis. This parameter adaptation allows the system to maintain universal applicability while achieving optimal performance on diverse datasets by tailoring parameters to each dataset's characteristics.
Data Source
AI summary
According to one embodiment, a method, computer system, and computer program product for generating anchors and selecting feature maps for a multi-scale object detection program based on analysis of the dataset is provided. The present invention may include generating a scale distribution of one or more scales of ground-truth objects, and, based on the scale distribution, dividing the effective scale range into a number of anchors greater than zero; furthermore, the invention may include generating a ratio distribution of ratios of the ground-truth objects; based on the ratio distribution, generating a ratio for at least one of the number of anchors; determining a template scale of one or more feature maps; and assigning the number of anchors to the feature maps based on the relative size of a scale of an anchor matching the relative size of a template scale of a feature map.