Two-Stage Neural Network for Autonomous Vehicle Object Detection
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately detecting and classifying objects in their environment using existing neural network systems, which often require significant computational resources and struggle with balancing precision and recall in real-time object detection.
Innovation Solution
A high precision object detection system utilizing a two-stage neural network architecture that includes a high-recall phase for initial object candidate generation and a high-precision phase for accurate classification, leveraging both laser and camera sensor data, and employing sub-neural networks to process and combine sensor inputs efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single-stage high-precision neural network is used for object detection, then detection accuracy is improved, but computational resources and processing time increase significantly
Solution Approach 1:
The object detection system is segmented into two distinct stages: a first neural network stage that performs initial object candidate detection with high recall, and a second neural network stage that performs precise classification and verification. This segmentation allows each stage to be optimized for its specific function, achieving high overall accuracy without requiring the entire system to operate at maximum computational capacity simultaneously.
Solution Approach 2:
The first neural network stage performs preliminary detection to identify potential object candidates and generate candidate regions before the second stage processes them. This preliminary action filters out non-object regions early in the pipeline, reducing the computational burden on the second stage and enabling high-precision detection only where needed, thus improving overall processing efficiency.
2Measurement precision
If a two-stage neural network system is implemented, then object detection precision is improved, but system complexity increases
Solution Approach 1:
The system architecture is segmented into two specialized neural networks with distinct functions: the first network focuses on candidate generation with high recall, while the second network focuses on precise classification. This functional segmentation allows each component to be simpler and more specialized, reducing the complexity burden compared to a single monolithic high-precision network that would need to handle all functions simultaneously.
Solution Approach 2:
The first neural network stage performs excessive detection by generating more candidate regions than ultimately needed, ensuring high recall and capturing all potential objects. This partial action at the first stage (detecting all possibilities) is followed by refinement in the second stage, allowing the system to achieve high precision without requiring either stage to be overly complex.
3Reliability
If high recall detection is performed first, then more object candidates are identified, but false positive rate increases
Solution Approach 1:
The detection process is segmented into two stages with different optimization goals: the first stage optimizes for high recall by identifying all potential object candidates regardless of precision, while the second stage optimizes for precision by verifying and filtering the candidates from the first stage. This segmentation allows the system to achieve both high recall and low false positive rate through sequential processing.
Solution Approach 2:
The second neural network stage provides feedback verification to the candidates generated by the first stage. The second stage analyzes the candidate regions and provides confirmation or rejection decisions, effectively filtering false positives while preserving true positives. This feedback mechanism allows the system to maintain high recall from the first stage while achieving high precision in the final detection results.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating object detection predictions from a neural network. In some implementations, an input characterizing a first region of an environment is obtained. The input includes a projected laser image generated from a three-dimensional laser sensor reading of the first region, a camera image patch generated from a camera image of the first region, and a feature vector of features characterizing the first region. The input is processed using a high precision object detection neural network to generate a respective object score for each object category in a first set of one or more object categories. Each object score represents a respective likelihood that an object belonging to the object category is located in the first region of the environment.