DEEP LEARNING AND MACHINE LEARNING INTEGRATION FOR OBJECT RECOGNITION

Abstract

The invention relates to a system and method for integrating deep learning (DL) and machine learning (ML) techniques to improve object recognition. By combining the feature extraction capabilities of deep learning models, such as convolutional neural networks (CNNs), with the classification power of machine learning models, such as support vector machines (SVMs), the system enhances both the accuracy and efficiency of object recognition tasks. The deep learning model extracts relevant features from raw input data, while the machine learning model classifies these features to recognize objects. A fusion layer optimizes the integration of both models, refining the final recognition decision for improved performance in real-time applications.

Specification

Description:In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.

The ensuing description provides exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.

Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

The word "exemplary" and/or "demonstrative" is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as "exemplary" and/or "demonstrative" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms "includes," "has," "contains," and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term "comprising" as an open transition word without precluding any additional or other elements.

Reference throughout this specification to "one embodiment" or "an embodiment" or "an instance" or "one instance" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

The invention relates to a system and method for integrating deep learning (DL) and machine learning (ML) models to enhance object recognition in various applications such as security surveillance, autonomous vehicles, robotics, and more. The integration leverages the strengths of both DL, which excels at automatic feature extraction, and ML, which is efficient in classification tasks. This hybrid approach not only improves recognition accuracy but also optimizes system performance in terms of computational efficiency and real-time processing capabilities.
The proposed system consists of two main modules: the deep learning module and the machine learning module, connected through an intermediate fusion layer. The deep learning module is responsible for processing the raw input data (such as images or video frames) and extracting high-level features, typically using a pre-trained deep learning model like a Convolutional Neural Network (CNN). The CNN learns hierarchical features from the raw data and generates feature vectors that represent different aspects of the input object.

The machine learning module takes these feature vectors as input and applies a classification algorithm (such as a Support Vector Machine (SVM), Random Forest, or k-Nearest Neighbors) to classify the objects based on the extracted features. The machine learning model may also include additional processing steps like outlier detection or data normalization to improve classification accuracy.

A key aspect of the invention is the fusion layer, which combines the outputs of both the deep learning and machine learning models. The fusion layer can be designed to merge the probability distributions or decision scores from each model, either by using weighted averaging, voting mechanisms, or a more sophisticated optimization technique. This step ensures that both models contribute to the final object recognition decision, enhancing accuracy and robustness, especially in complex or ambiguous scenarios.

The integration process is optimized to minimize computational overhead by utilizing the most suitable model for each task. While deep learning models typically require substantial computational resources for training, the machine learning models are more efficient during inference, particularly when classifying large datasets or when real-time processing is necessary.

The deep learning model is initially trained on a large labeled dataset using backpropagation, a supervised learning technique. During this phase, the model learns to extract hierarchical features from the input data, such as edges, textures, and shapes, which are essential for distinguishing between different objects.

Once the deep learning model is trained, the machine learning model is trained using the features extracted by the CNN. The machine learning algorithm can then learn to map these features to specific object classes. This two-stage training process allows the system to benefit from the strong feature extraction capabilities of deep learning and the efficient decision-making capabilities of machine learning.

To further enhance the system, an optimization layer can be implemented to fine-tune the interaction between the deep learning and machine learning models. This optimization may involve adjusting the weights of the fusion layer or the learning rates of individual models to balance the contributions of both the DL and ML components. The optimization process may also include regularization techniques to avoid overfitting and ensure that the model generalizes well to new, unseen data.

In one embodiment, the system is used for object classification in surveillance systems, where the goal is to automatically detect and identify objects such as people, vehicles, or other items of interest from video streams. The deep learning module, a CNN, processes each frame from the video feed and extracts high-level features representing objects in the scene. These features are then passed to the machine learning module, which classifies the objects based on pre-defined categories, such as "person," "car," or "bicycle." The fusion layer refines the decision by combining the results of both the deep learning and machine learning models, ensuring higher accuracy in real-time detection. The system is designed to operate efficiently, even with limited computational resources, by using lightweight machine learning models for classification while offloading the feature extraction task to the deep learning model.

In another embodiment, the system is applied in autonomous vehicles for object recognition tasks, such as detecting pedestrians, other vehicles, road signs, and obstacles. The deep learning model is trained on large datasets of labeled images or video streams captured from the vehicle's cameras. The CNN processes real-time video frames and extracts features related to the objects in the vehicle's environment. The machine learning model, such as an SVM, is then used to classify these objects based on the features extracted by the CNN. The fusion layer combines the outputs of the two models to provide a final classification, ensuring that the vehicle can make accurate decisions based on its surroundings. This embodiment is designed to optimize for real-time processing, enabling quick responses for critical decisions in autonomous navigation.

While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the invention and not as limitation. , Claims:1.A method for integrating deep learning and machine learning models for object recognition, comprising:
Extracting features from an input image using a deep learning model;
Classifying the extracted features using a machine learning model;
Combining the outputs of the deep learning model and the machine learning model to generate a final recognition decision.

2.The method of claim 1, wherein the deep learning model is a convolutional neural network (CNN), and the machine learning model is selected from the group consisting of support vector machines (SVM), random forests, and k-nearest neighbors.

3.The method of claim 1, further comprising an optimization step that adjusts the integration of the deep learning and machine learning outputs to improve classification accuracy.

4.A computer-readable medium having stored thereon instructions that, when executed by a processor, perform the method of claim 1 for object recognition.

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