Traffic Light Detection Using Neural Network Classification
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Solution Overview
Problem
Current systems fail to reliably detect and classify traffic lights, posing significant safety risks due to potential driver distraction or autonomous vehicle misinterpretation, especially when encountering various types of traffic lights.
Innovation Solution
A vehicle-mounted system utilizing on-board sensors, such as cameras, and an artificial neural network for real-time image-based detection and classification of traffic lights, which processes image data to control vehicle functions like warning systems or braking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional detection methods are used, then system complexity is low, but detection reliability is insufficient
Solution Approach 1:
The patent replaces traditional mechanical/optical detection systems with an artificial neural network-based computer vision system. The neural network processes image data from cameras to detect and classify traffic lights, substituting complex algorithmic processing for simpler hardware-based traditional methods, thereby improving reliability while managing system complexity through software-based intelligence.
2Speed
If real-time processing is implemented, then response speed is improved, but computational complexity increases
Solution Approach 1:
The patent implements preliminary action by pre-training the artificial neural network with extensive traffic light image data before deployment. This pre-processing of learning data allows the system to perform rapid real-time detection during actual operation, as the neural network has already learned traffic light patterns and can quickly classify new images without requiring complex real-time computation for learning.
3Measurement precision
If comprehensive classification is performed, then classification accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies segmentation by dividing the classification task into distinct categories (e.g., traffic light types, states, and configurations). The artificial neural network is trained to recognize and classify different traffic light scenarios separately, allowing for comprehensive classification accuracy while optimizing processing time by handling each category through specialized detection pathways within the network architecture.
Data Source
AI summary
A method is disclosed for detecting and classifying one or more traffic lights. The method may include converting an RGB frame to an HSV frame. The HSV frame may be filtered by at least one threshold value to obtain at least one saturation frame. At least one contour may be extracted from the at least one saturation frame. Accordingly, a first portion of the RGB may be cropped in order to encompass an area including the at least one contour. The first portion may then be classified by an artificial neural network to determined whether the first portion corresponds to a not-a-traffic-light class, a red-traffic-light class, a green-traffic-light class, a yellow-traffic-light class, or the like.