Autonomous Vehicle Object Recognition Using Onboard Neural Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current automated object recognition systems, particularly in underwater environments, lack the accuracy and adaptability to replace human operators effectively, as they rely on traditional computer vision algorithms that require extensive preprocessing and are not suited for unique domains like underwater settings.
Innovation Solution
The development of an unmanned autonomous vehicle equipped with a computationally-efficient deep neural network (DNN) for automatic target recognition, which learns to identify specific objects without extensive preprocessing and can operate in real-time using on-board resources, providing pixel-level representation of target importance and reducing communication bandwidth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional computer vision algorithms are used for automated object recognition, then the system can operate with standard image processing methods, but the accuracy is insufficient to replace human operators
Solution Approach 1:
The patent replaces traditional mechanical computer vision algorithms with a biological-inspired neural network system. The neural network mimics human visual processing by learning features directly from images through multiple layers of neurons, substituting conventional image enhancement and pattern recognition methods with a biologically-inspired approach that achieves human-level accuracy in object recognition
Solution Approach 2:
The patent transforms the recognition system by changing fundamental parameters: instead of using fixed thresholds and hand-crafted features, the system learns optimal parameters dynamically through training. The neural network adjusts weights and biases based on training data, enabling adaptive parameter optimization that improves accuracy across different object classes and environmental conditions
2Measurement precision
If highly trained operators perform imagery analysis, then accurate object detection is achieved, but the process requires hours of time and human resources
Solution Approach 1:
The neural network system performs self-learning and self-optimization through automated training processes. The system automatically adjusts its internal parameters, learns from training data, and improves its recognition capabilities without human intervention during operation, enabling autonomous accurate detection that replaces manual operator analysis
Solution Approach 2:
The patent implements preliminary training of the neural network with extensive labeled data before deployment. This preliminary action pre-configures the system with learned features and patterns, enabling it to perform accurate real-time detection without requiring operators to spend hours analyzing each image during operational use
3Adaptability or versatility
If standard image enhancement methods are applied, then basic preprocessing is achieved, but the system lacks adaptability for unique domains such as underwater environments
Solution Approach 1:
The neural network architecture provides universal adaptability across different environments and object types. A single trained network can recognize diverse objects (ships, submarines, marine life, debris) in various conditions (underwater, different lighting, various depths), replacing multiple specialized algorithms that would be needed for each specific domain
Solution Approach 2:
The system implements dynamic adaptability through the neural network's ability to learn and adjust to different environmental conditions. The network can be retrained or fine-tuned for specific domains like underwater environments, and its internal representations adapt dynamically based on the statistical properties of the input data, enabling versatility without rigid domain-specific programming
4Measurement precision
If complex preprocessing is performed to improve recognition accuracy, then detection precision increases, but the processing time and computational load increase significantly
Solution Approach 1:
The neural network performs feature extraction and preprocessing implicitly during its forward propagation through multiple layers. Instead of separate preprocessing steps, the network learns hierarchical features directly from raw images, with early layers capturing edges and textures and deeper layers capturing complex object structures, eliminating the need for manual preprocessing while maintaining accuracy
Solution Approach 2:
The patent merges multiple separate processing functions into the unified neural network architecture. Feature extraction, classification, and detection are combined into a single end-to-end system that processes images through multiple layers simultaneously, reducing the sequential processing steps and improving throughput while maintaining high accuracy
Data Source
AI summary
A platform is positioned within an environment. The platform includes an image capture system connected to a controller implementing a neural network. The neural network is trained to associate visual features within the environment with a target object utilizing a known set of input data examples and labels. The image capture system captures input images from the environment and the neural network recognizes features of one or more of the input images that at least partially match one or more of the visual features within the environment associated with the target object. The input images that contain the visual features within the environment that at least partially match the target object are labeled, a geospatial position of the target object is determined based upon pixels within the labeled input images, and a class activation map is generated, which is then communicated to a supervisory system for action.