AI-Driven Image Processing Method for Dynamic Object Detection in Unstructured Environments

Abstract

This invention presents an AI-Driven Image Processing Method for Dynamic Object Detection in Unstructured Environments. This invention discloses an AI-driven image processing system for real-time dynamic object detection in unstructured environments. The system includes a multi-scale feature extraction module, transformer-based attention mechanisms, and reinforcement learning to optimize detection accuracy across varying environmental conditions. Leveraging convolutional neural networks, attention-based processing, and adaptive learning, the system provides accurate object detection, classification, and tracking, making it suitable for autonomous vehicles, robotics, and surveillance applications. Accompanied Drawing [FIG. 1]

Specification

Description:[001] This invention relates to computer vision, artificial intelligence (AI), and autonomous systems, focusing on dynamic object detection in unstructured or complex environments. It particularly applies to real-time image processing using advanced AI models to detect, classify, and track objects in dynamic scenarios such as outdoor settings, autonomous driving, robotics, and surveillance.
BACKGROUND OF THE INVENTION
[002] The following description provides the information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[003] Object detection and recognition in real-world environments are crucial for various applications, such as autonomous vehicles, drones, robotics, and surveillance systems. In unstructured environments, where conditions are unpredictable, detecting and tracking objects becomes challenging due to varying lighting conditions, cluttered backgrounds, occlusions, and dynamic obstacles. Traditional image processing techniques often fall short due to their inability to adapt to complex and fast-changing scenes.
[004] Modern AI techniques, including convolutional neural networks (CNNs), transformers, and recurrent neural networks (RNNs), have proven effective in extracting features from high-dimensional data and identifying patterns. These techniques, particularly when combined with attention mechanisms, have improved object detection and tracking accuracy in real time, even under challenging conditions.
[005] To address the limitations of traditional methods, this invention leverages a combination of convolutional neural networks, transformer-based models, and reinforcement learning to create a robust and adaptable system for dynamic object detection. The system utilizes multi-scale feature extraction, attention mechanisms, and real-time feedback to adapt to changing conditions and ensure accurate detection, classification, and tracking of objects.
[006] Accordingly, to overcome the prior art limitations based on aforesaid facts. The present invention provides an AI-Driven Image Processing Method for Dynamic Object Detection in Unstructured Environments. Therefore, it would be useful and desirable to have a system, method and apparatus to meet the above-mentioned needs.

SUMMARY OF THE PRESENT INVENTION
[007] The proposed invention introduces an AI-driven image processing system designed for real-time dynamic object detection in unstructured environments. The system comprises a series of image processing and machine learning modules, including:
[008] Image Acquisition and Preprocessing: Captures real-time images using high-resolution cameras and preprocesses them for optimal feature extraction.
[009] Multi-Scale Feature Extraction: Utilizes convolutional neural networks to detect features across multiple scales, enhancing detection accuracy for objects of varying sizes.
[010] Attention Mechanism and Transformer-Based Processing: Employs transformer-based architectures and attention mechanisms to improve focus on relevant regions in each frame, enhancing detection efficiency.
Object Classification and Tracking: Uses deep learning models to classify detected objects and track their movement across frames.
[011] Dynamic Environment Adaptation via Reinforcement Learning: Incorporates reinforcement learning to adapt to real-time changes in the environment, improving detection robustness in varying conditions.
[012] The system is capable of processing complex, unstructured scenes, detecting, classifying, and tracking dynamic objects in real-time with high accuracy. By integrating attention-based deep learning with reinforcement learning, the invention enhances object detection capabilities, reduces false positives, and ensures that the system adapts continuously to changing environmental factors.
[013] In this respect, before explaining at least one object of the invention in detail, it is to be understood that the invention is not limited in its application to the details of set of rules and to the arrangements of the various models set forth in the following description or illustrated in the drawings. The invention is capable of other objects and of being practiced and carried out in various ways, according to the need of that industry. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.
[014] These together with other objects of the invention, along with the various features of novelty which characterize the invention, are pointed out with particularity in the disclosure. For a better understanding of the invention, its operating advantages and the specific objects attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated preferred embodiments of the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[015] The invention will be better understood and objects other than those set forth above will become apparent when consideration is given to the following detailed description thereof. Such description makes reference to the annexed drawings wherein:
[016] Figure 1: Block diagram of the AI-driven image processing system, showing the flow from image acquisition through preprocessing, feature extraction, object detection, and tracking, in accordance with an embodiment of the present invention, in accordance with an embodiment of the present invention.
[017] Figure 2: Flowchart illustrating the multi-scale feature extraction process using convolutional neural networks, in accordance with an embodiment of the present invention, in accordance with an embodiment of the present invention.
[018] Figure 3: Schematic of the transformer-based attention mechanism for focusing on relevant image regions, in accordance with an embodiment of the present invention.
[019] Figure 4: Diagram of the reinforcement learning adaptation module, highlighting how the system adjusts detection parameters based on real-time feedback, in accordance with an embodiment of the present invention.
[020] Figure 5: Example output showing object detection, classification, and tracking results in a dynamic, unstructured environment, in accordance with an embodiment of the present invention.
DETAILED DESCRIPTION OF THE INVENTION
[021] While the present invention is described herein by way of example using embodiments and illustrative drawings, those skilled in the art will recognize that the invention is not limited to the embodiments of drawing or drawings described and are not intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claims. As used throughout this description, the word "may" is used in a permissive sense (i.e. meaning having the potential to), rather than the mandatory sense, (i.e. meaning must). Further, the words "a" or "an" mean "at least one" and the word "plurality" means "one or more" unless otherwise mentioned. Furthermore, the terminology and phraseology used herein is solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and additional subject matter not recited, and is not intended to exclude other additives, components, integers or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles and the like is included in the specification solely for the purpose of providing a context for the present invention. It is not suggested or represented that any or all of these matters form part of the prior art base or are common general knowledge in the field relevant to the present invention.
[021] In this disclosure, whenever a composition or an element or a group of elements is preceded with the transitional phrase "comprising", it is understood that we also contemplate the same composition, element or group of elements with transitional phrases "consisting of", "consisting", "selected from the group of consisting of, "including", or "is" preceding the recitation of the composition, element or group of elements and vice versa.
[022] The present invention is described hereinafter by various embodiments with reference to the accompanying drawings, wherein reference numerals used in the accompanying drawing correspond to the like elements throughout the description. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiment set forth herein. Rather, the embodiment is provided so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those skilled in the art. In the following detailed description, numeric values and ranges are provided for various aspects of the implementations described. These values and ranges are to be treated as examples only and are not intended to limit the scope of the claims. In addition, a number of materials are identified as suitable for various facets of the implementations. These materials are to be treated as exemplary and are not intended to limit the scope of the invention.
This invention presents an advanced image and video compression system that combines hybrid neural networks (Variational Autoencoders, Generative Adversarial Networks, and Transformers) with quantum computing and edge computing for enhanced efficiency and scalability. The system compresses data by encoding it into a latent space, reconstructing high-quality images, and capturing dependencies across video frames. Quantum processors handle intensive computations, while edge computing facilitates real-time compression closer to data sources. Auxiliary data and meta-learning optimize compression for varying content, and a reinforcement learning agent ensures adaptive data flow in fluctuating network conditions. This system is suited for applications requiring high-quality, low-latency compression, such as streaming, telemedicine, and AR/VR.
System Overview
[023] 1. Image Acquisition and Preprocessing
The system captures images using high-resolution cameras equipped with image stabilization and noise reduction features, ensuring high-quality input for further processing. The preprocessing step includes:
• Normalization: Standardizes image data to enhance contrast and visibility of objects under varying lighting conditions.
• Noise Reduction: Applies filters to remove environmental noise, such as rain, dust, or low-light distortions.
• Augmentation: Uses real-time data augmentation (e.g., scaling, rotation) to improve the robustness of the model against orientation and scale variations.
[024] 2. Multi-Scale Feature Extraction
The multi-scale feature extraction module utilizes a convolutional neural network (CNN) designed to detect features at different spatial scales. This architecture ensures that objects of varying sizes, from small obstacles to larger structures, are accurately detected. Key components include:
• Multi-Layer Convolutions: The CNN consists of multiple convolutional layers, each tuned to detect features at specific scales.
• Feature Pyramid Networks (FPN): FPNs are used to construct a feature hierarchy, enabling the system to aggregate and analyze information across scales.
• Pooling Layers: Max-pooling layers enhance spatial invariance, ensuring consistent detection across varying positions in the image.
This module is critical for detecting objects of different sizes and distances, improving the model's generalizability in unstructured environments.
[025] 3. Attention Mechanism and Transformer-Based Processing
To improve detection accuracy, the system incorporates a transformer-based attention mechanism. Transformers enable the model to focus selectively on relevant parts of the image, reducing processing time and enhancing focus on objects of interest. Key components include:
• Self-Attention Mechanism: Computes attention scores across different image regions, allowing the model to prioritize areas with high information density.
• Positional Encoding: Maintains spatial information, ensuring that the model recognizes the relative position of objects within the image.
• Encoder-Decoder Structure: The transformer model's encoder-decoder architecture allows for efficient feature extraction and improves robustness in recognizing complex patterns.
This approach increases detection accuracy by concentrating on critical areas, especially in cluttered or low-contrast scenes.
[026] 4. Object Classification and Tracking
Once objects are detected, the system classifies them into predefined categories (e.g., pedestrians, vehicles, obstacles) using a deep neural network trained on a comprehensive dataset. The tracking module then follows each object's movement across frames, ensuring real-time updates on location and behavior.
• Object Classification: The classification model assigns a label to each detected object, with categories updated as new objects appear.
• Kalman Filter and Optical Flow: For tracking, the system employs a Kalman filter combined with optical flow techniques to predict object positions and handle occlusions effectively.
• Re-Identification Mechanism: If an object exits the frame and reappears, the re-identification algorithm confirms continuity, reducing redundancy in detection.
This module ensures continuity in detection and classification, crucial for applications such as autonomous navigation and monitoring.
[027] 5. Dynamic Environment Adaptation via Reinforcement Learning
To enhance adaptability, the system incorporates reinforcement learning (RL), enabling it to adjust detection parameters based on environmental changes. Using an RL model, the system continually learns from real-time data, adapting to diverse and evolving conditions such as changes in lighting, weather, or background complexity.
• Reward Mechanism: The RL model receives positive feedback for correct detections and penalizes false positives, optimizing detection accuracy over time.
• Policy Adjustment: The model updates its detection and tracking policies based on feedback, ensuring that performance improves as the system encounters varied scenarios.
• Continuous Learning: The RL model's ability to learn continuously enables the system to adapt to novel environments without requiring manual recalibration.
[017] This self-improving component makes the system highly resilient and effective across different unstructured environments, offering superior performance in complex, real-world applications.
[017] It is to be understood that the above description is intended to be illustrative, and not restrictive. For example, the above-discussed embodiments may be used in combination with each other. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description.
[028] The benefits and advantages which may be provided by the present invention have been described above with regard to specific embodiments. These benefits and advantages, and any elements or limitations that may cause them to occur or to become more pronounced are not to be construed as critical, required, or essential features of any or all of the embodiments.
[029] While the present invention has been described with reference to particular embodiments, it should be understood that the embodiments are illustrative and that the scope of the invention is not limited to these embodiments. Many variations, modifications, additions and improvements to the embodiments described above are possible. It is contemplated that these variations, modifications, additions and improvements fall within the scope of the invention.
, Claims:1. An AI-driven image processing system for dynamic object detection in unstructured environments, comprising:
An image acquisition module configured to capture high-resolution images;
A preprocessing module designed to normalize, reduce noise, and augment the images;
A multi-scale feature extraction module using a convolutional neural network to detect features at multiple scales.
2. The system of Claim 1, further comprising a transformer-based attention mechanism to selectively focus on regions of interest within each image, enhancing object detection efficiency.
3. The system of Claim 1, wherein the multi-scale feature extraction module includes a feature pyramid network for aggregating information across different spatial scales.
4. The system of Claim 1, further comprising an object classification and tracking module that uses a deep neural network to classify objects and track their movement across frames.
5. The system of Claim 1, further comprising a reinforcement learning module configured to adapt detection parameters based on real-time feedback, optimizing detection in dynamic environments.
6. The system of Claim 1, wherein the preprocessing module applies real-time data augmentation techniques to improve model robustness against variations in orientation and scale.
7. The system of Claim 1, wherein the object tracking module combines a Kalman filter and optical flow to ensure accurate tracking even during partial occlusions.

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