Two-Stage Neural Network for Pedestrian Detection
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Solution Overview
Problem
Current pedestrian detection systems in vehicles face challenges in achieving accurate, fast, and cost-effective pedestrian detection, often resulting in false alerts and high power consumption, which can be distracting and costly due to the need for expensive GPUs.
Innovation Solution
A two-stage classifier system using a first neural network for coarse pedestrian identification and a second neural network for fine classification, optimizing image data processing and reducing false alerts by focusing processing on potential pedestrian areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single high-accuracy neural network is used for pedestrian detection, then detection accuracy is improved, but processing speed decreases and power consumption increases
Solution Approach 1:
The patent divides the pedestrian detection task into two separate neural network stages: a first neural network for initial detection and a second neural network for refined classification. This segmentation allows each network to be optimized for its specific function, with the first network handling coarse detection quickly and the second network providing accurate classification only for detected regions, thereby maintaining high accuracy while improving processing speed and reducing power consumption.
2Measurement precision
If a single high-accuracy neural network is used for pedestrian detection, then detection accuracy is improved, but device cost increases due to expensive GPUs
Solution Approach 1:
The patent divides the pedestrian detection task into two separate neural network stages: a first neural network for initial detection and a second neural network for refined classification. This segmentation allows each network to be optimized for its specific function, with the first network handling coarse detection quickly and the second network providing accurate classification only for detected regions, thereby maintaining high accuracy while improving processing speed and reducing power consumption.
3Reliability
If all detected objects are classified as pedestrians, then no false alerts are missed, but false alerts increase causing driver distraction
Solution Approach 1:
The patent applies different processing quality to different regions of the image. The first neural network performs a coarse detection with lower quality requirements across the entire image, while the second neural network performs high-quality classification only on the detected regions of interest. This local quality differentiation ensures reliable pedestrian detection while reducing false alerts by applying detailed analysis only where necessary.
Solution Approach 2:
Instead of directly classifying all detected objects as pedestrians (which causes false alerts), the patent inverts the approach by first detecting potential regions and then applying refined classification only to those regions. This inverted two-stage process reduces false alerts by distinguishing actual pedestrians from other objects through the second neural network's specialized classification.
Data Source
AI summary
Driver and pedestrian safety can be aided by systems and methods to provide identification and classification of objects in a vehicle travel path. Information about classified objects can be shared with a human driver to inform the driver about potentially hazardous conditions, or the information can be interpreted automatically by an operating system of the vehicle. In an example, a camera coupled to a vehicle can receive images from an image sensor. A computer system can use machine learning and neural network-based processing to identify an object present in the images and determine whether the object is a pedestrian. In an example, the computer system can process information from a region of interest in the images that comprises less than an entire field of view in the images.