CNN Lane Violation Detection Using Multi-Headed Lane Segmentation
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Solution Overview
Problem
Existing traffic enforcement systems struggle to accurately detect lane violations due to limitations in lane detection capabilities and high false positive rates, particularly in scenarios where vehicles park or stop in restricted lanes, impacting bus reliability and safety for cyclists, and are often resource-intensive and costly.
Innovation Solution
A system using convolutional neural networks to detect vehicles and lanes, employing a multi-headed second convolutional neural network for lane detection and a first neural network for vehicle bounding, with noise smoothing and pixel intensity analysis to determine lane occupancy, and utilizing over-the-air updates for model improvements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If traditional logic-based automated camera solutions are deployed to enforce traffic violations beyond intersections and crosswalks, then the field of view and enforcement capability are extended, but the false positive detection rate increases to up to 80%
Solution Approach 1:
The patent replaces traditional logic-based automated enforcement systems with deep learning-based computer vision models. Specifically, convolutional neural networks are used to detect vehicles and lanes, and a segmentation model determines lane occupancy, substituting rule-based logic with intelligent pattern recognition to reduce false positives while maintaining extended field of view
Solution Approach 2:
The system changes the detection parameters by using pixel intensity analysis and noise smoothing operations instead of traditional logical rules. The segmentation model analyzes the intensity values of pixels within the lane-of-interest polygon to determine occupancy, transforming the detection approach from binary logic to continuous parameter-based decision making
2Reliability
If specialized personnel are dedicated to enforce lane violations, then enforcement accuracy and reliability improve, but the cost and resource requirements increase significantly
Solution Approach 1:
The system enables self-service enforcement by deploying autonomous edge devices on buses that automatically detect lane violations using onboard cameras and deep learning models. The devices perform vehicle detection, lane detection, and occupancy determination without human intervention, and can issue citations automatically, eliminating the need for specialized enforcement personnel while maintaining high reliability
Solution Approach 2:
The patent substitutes human enforcement personnel with automated deep learning-based detection systems. The convolutional neural networks and segmentation models perform the enforcement function that previously required specialized personnel, significantly reducing resource investment while maintaining or improving enforcement reliability through consistent automated operation
3Adaptability or versatility
If models trained for recognizing objects such as vehicles, pedestrians, or traffic lights are used, then object detection capability is available, but lane detection capability is insufficient
Solution Approach 1:
The patent applies segmentation by dividing the image processing task into distinct components: a first convolutional neural network detects vehicles and generates bounding boxes, a second convolutional neural network detects lanes and generates polygon representations, and a segmentation model determines lane occupancy by analyzing pixel intensities within the lane polygon. This segmented approach allows each model to specialize in its specific detection task, achieving both object and lane detection capabilities
Solution Approach 2:
The system introduces an intermediary segmentation model that bridges object detection and lane violation determination. The segmentation model takes the lane polygon from the second convolutional neural network and the vehicle bounding box from the first network, then analyzes pixel intensities within the lane area to determine occupancy, serving as a mediator that combines multiple detection capabilities to solve the lane detection problem
Data Source
AI summary
Disclosed herein are systems, methods, and devices for detecting traffic lane violations. In one embodiment, a method for detecting a potential traffic violation is disclosed comprising bounding a vehicle detected from one or more video frames of a video in a vehicle bounding box. The vehicle can be detected and bounded using a first convolutional neural network. The method can also comprise bounding, using the one or more processors of the edge device, a plurality of lanes of a roadway detected from the one or more video frames in a plurality of polygons. The plurality of lanes can be detected and bounded using multiple heads of a multi-headed second convolutional neural network. The method can further comprise detecting a potential traffic violation based in part on an overlap of at least part of the vehicle bounding box and at least part of one of the polygons.