Small Object Detection with Single-Stage Soft Grouping
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Solution Overview
Problem
Existing object detection methods face challenges in efficiently detecting small objects in images and videos due to complex pipelines, computational inefficiencies, and inaccuracies in keypoint-based methods, particularly when dealing with occlusions and limited visual information.
Innovation Solution
A single-stage soft-grouping non-maximum suppression (SG-NMS) approach is introduced, merging grouping and NMS phases into a single stage and sharing distance metric calculations, allowing for a flexible number of corner points for improved accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If keypoint-based methods are used for small object detection, then detection capability for small objects is improved, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent merges the grouping phase and NMS phase into a single unified stage, eliminating the need for separate processing steps. This integration allows the system to perform both grouping and suppression operations simultaneously, reducing the number of computational passes required and thereby decreasing processing time while maintaining detection accuracy for small objects.
Solution Approach 2:
The patent introduces a flexible corner point selection mechanism that can adaptively determine the optimal number of corner points based on object characteristics. This universal approach allows the same detection framework to efficiently handle various object types and sizes, improving processing speed across different scenarios without sacrificing detection precision.
2Reliability
If traditional two-stage object detection pipelines are used, then detection thoroughness is improved, but device complexity and computational cost increase
Solution Approach 1:
The patent consolidates multiple detection stages into a single unified detection pipeline. By integrating the grouping operation and non-maximum suppression into one stage, the system achieves detection thoroughness comparable to two-stage methods while significantly reducing pipeline complexity and computational overhead.
Solution Approach 2:
The patent extracts and eliminates redundant intermediate processing steps from the traditional two-stage pipeline. By removing the separate grouping phase and directly integrating its functionality into the NMS stage, the system reduces computational complexity while preserving the essential detection capabilities needed for thorough object detection.
3Adaptability or versatility
If anchor-based methods are used for object detection, then detection coverage is improved, but computational efficiency deteriorates
Solution Approach 1:
Instead of generating multiple anchor boxes and filtering them through complex NMS operations, the patent inverts the approach by directly selecting corner points from detected features. This inversion eliminates the need for anchor box generation and associated computational steps, improving efficiency while maintaining comprehensive detection coverage through flexible corner point selection.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture are disclosed for small object detection in images and videos. An example apparatus for small object detection includes a memory, computer readable instructions, and at least one processor to execute the computer readable instructions to at least receive an input image, identify a first grouping reference box for a first object representation in the input image, the first grouping reference box based on feature extraction performed with a feature extractor network, extract a first coordinate and a second coordinate for a corner location from a heatmap, the heatmap used to determine the first grouping reference box, generate a second grouping reference box for the first object representation based on the corner location, and update the first grouping reference box with the second grouping reference box.