Automated Target Recognition via Self-Organizing Map Labeling
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Solution Overview
Problem
Current neural networks require a large number of manually labeled training images for accurate object detection and classification, which is time-consuming and prone to human error, especially when dealing with rare objects or multiple object types, limiting their effectiveness in real-world applications.
Innovation Solution
A method utilizing a self-organizing map classifier to automatically generate labels for unlabeled training images, reducing the need for manual labeling and enhancing the neural network's ability to detect, classify, and identify objects by aggregating image chips and converting them into image vectors for training, thereby improving the accuracy and efficiency of object detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling of training images is used, then the neural network can be trained with accurate labels, but the process is time-consuming and prone to human error
Solution Approach 1:
The system uses an automated labeling mechanism where the neural network itself generates labels for training images through self-organization and clustering algorithms, eliminating the need for manual human labeling while maintaining accuracy. The network performs self-service by automatically creating its own training data with consistent, error-free labels.
Solution Approach 2:
An automated labeling intermediary system is introduced between the training images and the neural network, using unsupervised learning algorithms to generate labels automatically. This intermediary eliminates direct human involvement in labeling while ensuring consistent and accurate label generation through algorithmic processes.
2Measurement precision
If a large quantity of training images is used to improve neural network accuracy, then the detection accuracy improves, but the training time and computational resources increase significantly
Solution Approach 1:
The system performs preliminary automated labeling of training images using unsupervised learning algorithms before the main training process. By pre-processing and automatically labeling images in advance, the system prepares high-quality training data without requiring manual labeling during the main training phase, thus reducing overall training time while maintaining accuracy.
Solution Approach 2:
The system changes the parameter of label generation from manual human process to automated algorithmic process. This parameter change enables rapid generation of large quantities of labeled training images, allowing the neural network to be trained on extensive datasets without proportionally increasing training time, as the automated labeling can be performed efficiently at scale.
3Adaptability or versatility
If multiple object types are classified by the neural network, then the versatility of the system improves, but the number of training images required scales proportionally
Solution Approach 1:
The system employs a universal automated labeling mechanism that can handle multiple object types simultaneously through a single unified process. The unsupervised learning algorithm performs multi-functionality by automatically labeling diverse object categories without requiring separate manual labeling procedures for each object type, thus enabling versatile classification while avoiding proportional increases in training data requirements.
Solution Approach 2:
The system uses copying by generating synthetic training examples through automated labeling and data augmentation techniques. Instead of requiring unique manual labels for each object type, the system creates copied and varied versions of labeled data through algorithmic processes, enabling the neural network to learn multiple object types from a manageable dataset size.
4Reliability
If manual labeling is performed to ensure accurate labels, then the quality of training data improves, but human errors are introduced into the training process
Solution Approach 1:
The system eliminates human involvement in labeling by implementing self-service automated labeling through unsupervised learning algorithms. The neural network automatically generates labels for training images without human intervention, ensuring consistent and error-free labeling. This self-service approach maintains high training data quality while completely avoiding human errors in the labeling process.
Data Source
AI summary
A method includes receiving, from an image sensor, an image, identifying, by a first neural network, a plurality of locations-of-interest within the image, and generating, by the first neural network, a first classification label for each location-of-interest of the plurality of locations-of-interest. The method also includes extracting, from the image, a plurality of image chips derived from the plurality of locations-of-interest and generating, by a second neural network, a second classification label for each image chip of the plurality of image chips. The method further includes determining an identification of a set of targets within the image using the plurality of locations-of-interest, the first classification label for each location-of-interest of the plurality of locations-of-interest, the plurality of image chips, and the second classification label for each image chip of the plurality of image chips, and transmitting the identification of the set of targets within the image.