Anchor-Free Object Detection via Point-Based Bounding Box Prediction
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Solution Overview
Problem
Existing object detection systems based on predefined anchor-boxes require hyper-parameter tuning and suffer from imbalances in positive/negative samples, leading to inefficiencies and high computational demands.
Innovation Solution
An object detection system that performs object detection and segmentation based on points, using a feature map extraction module to generate feature pyramids, a bounding box detection module to classify and predict bounding boxes without anchor-boxes, and a mask generation module to generate masks through attention maps and pooling operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If predefined anchor-box based object detection is used, then object detection can be performed, but hyper-parameter tuning is required and positive/negative sample imbalance occurs
Solution Approach 1:
The patent extracts and eliminates the anchor-box component from the object detection system. Instead of using predefined anchor-boxes, the method directly predicts bounding boxes from feature maps, removing the need for anchor-box configuration and hyper-parameter tuning while maintaining detection capability
Solution Approach 2:
The patent changes the fundamental parameters of the detection approach by replacing anchor-box-based prediction with point-based prediction. The bounding box parameters are directly regressed from feature map values at specific points, eliminating the need for multiple anchor-box configurations and associated hyper-parameters
2Productivity
If predefined anchor-box based object detection is used, then object detection can be performed, but computational requirements increase
Solution Approach 1:
The patent removes the computationally intensive anchor-box prediction mechanism and replaces it with direct bounding box prediction from feature maps. This eliminates the need to evaluate multiple anchor-box candidates and their associated loss functions, significantly reducing computational burden
Solution Approach 2:
Instead of evaluating multiple anchor-box configurations and performing extensive hyper-parameter tuning, the patent performs a single point-based prediction operation. This partial action approach achieves detection without the excessive computational requirements of anchor-box based methods
3Adaptability or versatility
If multiple anchor-box candidates are used, then detection coverage can be improved, but positive/negative sample imbalance occurs
Solution Approach 1:
The patent extracts and removes the anchor-box candidate mechanism that causes sample imbalance. By directly predicting bounding boxes from feature maps at specific points, the method eliminates the artificial creation of multiple anchor-box candidates and their associated positive/negative sample imbalances
Solution Approach 2:
Instead of generating multiple anchor-box candidates and then selecting the best one (which creates sample imbalance), the patent inverts the approach by directly predicting the bounding box parameters from feature map values at points. This inversion eliminates the need for candidate generation and associated sampling issues
Data Source
AI summary
Provided are an object detection system and an object detection method. An object detection system may include a feature map extraction module configured to receive an image for object detection and extract a feature map having multiple resolutions for the image; a bounding box detection module configured to classify a bounding box by applying a first group of convolutional layers to the feature map, and predict the bounding box by applying a second group of convolutional layers to the feature map; and a mask generation module configured to generate a mask for the shape of the object in the bounding box using the feature map.