Vehicle Object Detection Using Multi-Path Neural Networks
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Solution Overview
Problem
Existing object detection systems in vehicles face challenges in accurately identifying objects under less than ideal environmental conditions, such as dim lighting, atmospheric phenomena, and partial obscuration, leading to low confidence and incorrect object recognition.
Innovation Solution
A two-step object detection system utilizing multi-path object detection and a partially observable Markov decision process (POMDP) to enhance object detection accuracy, combining outputs from multiple neural networks and incorporating context data to refine object identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single neural network is used for object detection, then the system complexity is low, but the detection accuracy and confidence under challenging environmental conditions deteriorates
Solution Approach 1:
The detection system is segmented into multiple specialized neural networks (first neural network for initial detection, second neural network for refined detection, third neural network for verification) that process different aspects of object detection independently. Each network focuses on specific detection tasks, improving overall accuracy while maintaining manageable complexity through functional division.
Solution Approach 2:
Multiple neural network detection results are merged and combined through a confidence scoring mechanism. The system integrates detections from different networks, aggregates their confidence scores, and produces a unified high-confidence detection result, thereby improving reliability without requiring a single overly complex network.
2Reliability
If multiple neural networks are combined for detection, then the reliability of object detection improves, but the computational resources and processing time increase
Solution Approach 1:
The first neural network performs preliminary detection to identify potential objects of interest. By conducting initial filtering and identification before applying more computationally intensive second and third networks, the system reduces overall energy consumption while maintaining high reliability through staged processing.
Solution Approach 2:
The system applies multiple levels of detection (partial action) where not all neural networks process every image at full capacity. Instead, networks are selectively applied based on detection confidence thresholds and environmental conditions, reducing unnecessary computational energy waste while maintaining reliability for critical detections.
3Measurement precision
If traditional object detection methods are used, then the processing speed is fast, but the accuracy under less than ideal environmental conditions deteriorates
Solution Approach 1:
Context data (metadata about environmental conditions, scene information, and detection history) serves as an intermediary that guides the neural networks in challenging conditions. This intermediary information helps the system adapt its detection strategy without requiring complete re-processing of images, maintaining processing speed while improving accuracy in difficult environments.
Solution Approach 2:
The detection system dynamically adjusts its processing approach based on environmental conditions and detection confidence levels. When challenges are detected (low light, obscuration, etc.), the system activates additional neural networks and processing steps. When conditions are favorable, it uses faster, simpler detection paths, thereby optimizing the balance between accuracy and processing speed.
Data Source
AI summary
A computer that includes a processor and a memory, the memory including instructions executable by the processor to input an image to a first neural network to generate a first detected object and input the image to a second neural network to generate a reconstructed image which is input to a third neural network to generate a second detected object. The image can be divided into portions and the portions input to respective fourth neural networks to generate portions of a third detected object. The first detected object, the second detected object, the portions of the third detected object, and context data can be input to a partially observable Markov decision process to generate a high confidence detected object.