Traffic Light Classification via Semantic Keypoints
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Solution Overview
Problem
Conventional traffic light detection systems face challenges in complex urban scenarios due to increased computational difficulty and degraded performance, especially in environments with multiple traffic lights and varying configurations, requiring extensive annotation and resources for training datasets.
Innovation Solution
The system uses semantic keypoint representations to classify each bulb of a traffic light, bypassing the need for 2D bounding box detection, employing neural networks to determine semantic keypoints and classify light signals, which reduces annotation requirements and improves computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional 2D bounding box detection is used for traffic light classification, then the system can detect traffic lights in various configurations, but the computational difficulty increases and performance degrades in complex scenarios with multiple traffic lights
Solution Approach 1:
The patent segments the traffic light detection task into two distinct stages: first detecting the traffic light housing using 2D bounding box, then detecting individual bulbs within the housing using 3D bounding boxes. This segmentation allows the system to handle complex scenarios by breaking down the challenging task of direct bulb detection into manageable steps, reducing overall computational complexity while maintaining classification accuracy
Solution Approach 2:
The patent transitions from 2D bounding box detection to 3D bounding box detection for bulb localization. By adding the depth dimension, the system can accurately locate individual bulbs within the traffic light housing, enabling precise classification even in complex configurations with multiple traffic lights. This dimensional enhancement resolves the contradiction by providing the necessary spatial information without proportionally increasing computational burden
2Ease of manufacture
If conventional 2D bounding box detection is used, then the system structure remains simple, but annotation costs for large-scale training datasets increase significantly
Solution Approach 1:
The patent segments annotation requirements into two levels: traffic light housing boundaries and individual bulb locations. This segmentation enables more efficient annotation processes where annotators first mark the housing, then systematically identify bulbs within each housing. The structured approach reduces annotation time and costs for large-scale datasets compared to direct bulb-by-bulb detection
Solution Approach 2:
The patent performs preliminary detection of traffic light housings before detecting individual bulbs. This preliminary action creates a structured framework that guides subsequent bulb detection and annotation processes. By establishing housing boundaries first, the system reduces the search space for bulb annotation, thereby reducing both time and cost for preparing training datasets
Data Source
AI summary
A traffic light classification system for a vehicle includes an image capture device to capture an image of a scene that includes a traffic light with multiple light signals, a processor, and a memory communicably coupled to the processor and storing a first neural network module including instructions that when executed by the processor cause the processor to determine, based on inputting the image into a neural network, a semantic keypoint for each light signal in the traffic light, and determine, based on each semantic keypoint, a classification state of each light signal.