Multi-Stage Bounding Box Detection for Dense Occluded Objects
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Solution Overview
Problem
Existing object detectors struggle to generate high-quality bounding boxes, especially in dense or occluded scenarios, leading to inaccurate object localization, which is crucial for applications like smart transportation.
Innovation Solution
A multi-stage manual labeling learner (MLL) system simulates human-like object detection by using a two-stage process, involving heatmap estimation for the top-left corner, offset refinement, and distance regression for the bottom-right corner, followed by cascaded convolution blocks to refine the bounding box.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing object detectors are used, then detection speed is maintained, but bounding box accuracy deteriorates in dense or occluded scenarios
Solution Approach 1:
The detection process is segmented into multiple stages: first stage generates initial bounding box predictions, second stage refines these predictions through cascaded convolution blocks. This multi-stage segmentation allows the system to progressively improve bounding box accuracy, particularly for dense and occluded objects, without sacrificing overall detection speed.
Solution Approach 2:
The first stage performs preliminary bounding box generation and filtering, preparing candidate boxes that are then refined in the second stage. This preliminary action reduces the search space and focuses computational resources on the most promising candidates, improving both accuracy and efficiency in complex scenarios.
2Measurement precision
If multi-stage refinement process is implemented, then bounding box accuracy improves, but computational cost increases
Solution Approach 1:
The refinement process applies different levels of processing to different regions: high-confidence, easy-to-detect objects receive minimal refinement, while low-confidence or occluded objects undergo more intensive processing through the cascaded convolution blocks. This local quality approach optimizes computational resource allocation.
Solution Approach 2:
The system dynamically adjusts processing parameters based on detection confidence and object characteristics. The cascaded convolution blocks are selectively applied based on the quality of initial predictions, changing the intensity of refinement based on the specific detection scenario to balance accuracy and computational cost.
Data Source
AI summary
This disclosure describes systems, methods, and devices related to object detection in images. A device may input an image, representing an object, to a manual labeling learner system; identify, using the system, first coordinates of an upper left corner of a bounding box representing the object based on a heatmap indicative of a probability of the first coordinates representing the upper left corner; identify, using the system, second coordinates of a bottom right corner of the bounding box based on the first coordinates and a first distance regression map indicative of coordinate differences between the second coordinates and ground truth coordinates input to the machine learning model as training data; generate, using the system, adjustments to the first coordinates and the second coordinates based on a second regression map; and generate, using the system, the adjusted first and second coordinates, the bounding box.