Missed-Object Refinement Through Multi-Stage 3D Detector Training
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Solution Overview
Problem
State-of-the-art 3D object detectors suffer from false negative detections due to occlusions and cluttered backgrounds, which are not adequately addressed in current training processes, posing risks in autonomous driving applications.
Innovation Solution
A multi-stage training method for 3D object detectors that involves detecting objects in multiple stages, masking previously detected objects, and updating the detector based on loss to improve detection accuracy, focusing on undetected objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current training processes are used for 3D object detectors, then the detector can perform basic object detection, but false negative detections increase due to occlusions and cluttered backgrounds
Solution Approach 1:
The training process is segmented into multiple stages: a first stage for basic object detection and a second stage specifically for detecting missed objects. This segmentation allows the system to address false negatives systematically by separating the detection task into manageable phases, where the second stage focuses exclusively on refining detection accuracy by targeting objects missed in the first stage.
Solution Approach 2:
The method performs preliminary detection in the first stage to identify objects, then uses this initial detection results to guide the second stage training. By preparing and processing detection data from the first stage before implementing the second stage, the system efficiently addresses false negatives without requiring complete retraining, thus improving reliability while managing computational resources.
2Reliability
If multi-stage training is implemented to reduce false negatives, then detection accuracy improves, but training complexity and computational cost increase
Solution Approach 1:
The first stage of training performs preliminary object detection to create a baseline model. This preliminary detection results are then used to generate the training data for the second stage, eliminating the need to reprocess all training data from scratch. This approach reduces the effective complexity of multi-stage training by leveraging results from the first stage to guide the second stage.
Solution Approach 2:
The method extracts and focuses training attention on specific subsets of data - namely, the objects missed in the first stage detection. By extracting only the problematic cases (false negatives) for specialized training in the second stage, rather than processing all training data through both stages, the system manages computational complexity while still achieving improved false negative reduction.
Data Source
AI summary
3D objection detection is a computer vision task that generally refers to detecting (e.g. classifying and localizing) an object in 3D space from an image or video that captures the object. This computer vision task has many useful applications, such as autonomous driving applications which rely on the detection of 3D objects in a local environment to make autonomous driving decisions. State-of-the-art 3D object detectors generally rely on machine learning, but current training processes for these detectors do not specifically address false negative detections, or missed objects, which are often caused by occlusions and/or cluttered backgrounds in the given image/video. Reducing false negatives is crucial for many downstream applications, particularly autonomous driving applications which rely on accurate detection of obstacles for making safe driving decisions. The present disclosure provides for a multi-stage training process that reduces false negative detections by 3D object detectors.