DEEP LEARNING-BASED IMAGE RECOGNITION SYSTEM FOR REAL-TIME OBJECTION DETECTION

Abstract

The present invention discloses a deep learning-based image recognition system optimized for real-time object detection and process monitoring in dynamic environments. The system comprises a learning device and a monitoring device, which work together to achieve high-speed, accurate classification and abnormality detection. The learning device uses advanced neural processing, including convolutional and recurrent neural networks, to classify objects and store their attributes. The monitoring device includes a multi-view video acquisition system, an object detection module, and an abnormality detection unit, enabling continuous, lag-free monitoring. With features such as high-speed digital processing and FPGA-enabled abnormality detection, the system efficiently identifies and responds to anomalies in real time. A feedback loop continuously updates the learning models, ensuring adaptability to evolving patterns. This invention is applicable to high-stakes environments like manufacturing and autonomous systems, where precision and prompt anomaly detection are critical for safety and efficiency.

Specification

Description:[015] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments. On the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit, and scope of the present disclosure as defined by the appended claims.
[016] In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention may be practiced without some of these specific details.
[017] 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.
[018] 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.
[019] 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.
[020] 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.
[021] In an embodiment of the invention and referring to Figures 1, the present invention relates to a deep learning-based image recognition system optimized for real-time object detection and process monitoring, specifically designed to meet the demands of high-speed, high-accuracy classification and abnormality detection in dynamic environments. The system comprises a learning device and a monitoring device, each containing several novel hardware and software components that work synergistically to achieve continuous, adaptive monitoring across diverse applications.
[022] The learning device is equipped with a neural processing unit (NPU), a high-performance graphical processing unit (GPU), and specialized deep learning algorithms that enable the classification of objects within the process into three distinct types: moving objects, status objects, and vector objects. During initial setup, the learning device registers objects to be recognized in the process, storing their attributes and processing requirements in a dedicated memory unit. The learning device leverages a combination of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to train models that understand spatial and temporal features, enabling real-time feature extraction.
[023] The core architecture of the monitoring device consists of a multi-view video acquisition system, an object detection module, a process classification module, and an abnormality detection unit. The multi-view video acquisition system comprises an array of cameras, strategically placed around the equipment performing the process, to capture high-definition frames from multiple angles. This camera array is controlled by a central control unit that synchronizes frame capture, ensuring continuous, lag-free streaming to the object detection module. The high frame rate (e.g., 120 frames per second) supported by this system is crucial for accurately tracking fast-moving objects in real time.
[024] Data from the camera array is fed directly into the object detection module, which is supported by a high-speed digital signal processor (DSP) capable of processing and analyzing frames in parallel. The object detection module classifies objects in the process video into moving, status, and vector objects based on their behavior and spatial characteristics. For instance, a moving object may be a robotic arm, a status object might be a gauge or indicator, and a vector object could represent a directional marker. The detection algorithm continuously extracts features such as size, shape, color, and motion vectors from the frames to form feature sets specific to each object type.
[025] Once object features are extracted, they are transmitted to the process classification module, which operates with a database of pre-stored process patterns. The process classification module utilizes a similarity comparison algorithm, which leverages cosine similarity, dynamic time warping, or other similarity measures to match real-time feature patterns with stored process feature patterns. This matching is computed across multiple frames, allowing for robust real-time classification of the ongoing process. The module further incorporates a temporal loss function that tracks deviations across timestamps, comparing patterns to account for any dynamic shifts in the monitored process.
[026] The abnormality detection unit is a critical component, as it monitors the process for any discrepancies that indicate potential faults or disruptions. By comparing real-time feature patterns against pre-defined thresholds, the abnormality detection unit calculates a loss value for each object based on discrepancies in attributes such as motion or shape. If a loss value exceeds the threshold or persists over a certain time, an alert is generated, and the process is flagged for potential abnormality. This unit is supported by a custom field-programmable gate array (FPGA) to achieve low-latency response times.
[027] To validate the system's efficacy, a series of trials were conducted with varying process complexities. Table 1 shows the comparison of detection accuracy across different types of objects in simulated environments, indicating the system's robust adaptability to multiple use cases.

[028] The results in Table 1 demonstrate that the system achieves high accuracy and low latency across various object types, proving its utility for real-time applications. Furthermore, Table 2 highlights the system's performance in abnormality detection across different scenarios, showcasing the detection rate and latency when abnormalities were introduced.

[029] Each component's seamless integration enables the system to operate in real time. The NPU of the learning device is linked with the DSP in the object detection module of the monitoring device via a high-speed PCIe bus, facilitating efficient data flow between these components. The inter-component communication is further managed by a proprietary software stack, which ensures that each module synchronizes data without bottlenecks, allowing for rapid adaptation to changes in the process environment.
[030] The deep learning models trained on the learning device are periodically updated based on new data patterns and anomalies flagged during operation. This adaptive learning is achieved through a feedback loop where the monitoring device sends identified abnormalities back to the learning device for retraining. The retrained model is then pushed to the monitoring device to refine the feature extraction and classification functions continuously, ensuring the system remains responsive to new patterns.
[031] Another novel aspect of this invention is its modular software framework, which consists of an operating environment that supports multiple deep learning libraries, such as TensorFlow, PyTorch, and Keras. This flexibility allows for the integration of customized models, enabling further specialization depending on the target application. The software is developed using an efficient pipeline structure, where the data flow from object detection to process classification to abnormality detection is handled through interconnected queues and buffers, minimizing latency and optimizing throughput.
[032] The system's application potential is vast, ranging from manufacturing process monitoring, where high-speed detection of anomalies is essential, to autonomous navigation systems that require accurate real-time object recognition. The high detection rate and low latency make the system particularly suitable for high-stakes environments, where failure to detect an abnormality promptly could result in significant downtime or safety risks.
[033] In conclusion, the deep learning-based image recognition system for real-time object detection described here represents a substantial advancement in the field of process monitoring and control. By integrating novel hardware components such as NPUs, DSPs, and FPGAs, along with state-of-the-art deep learning algorithms, the invention achieves high performance and adaptability across diverse application environments. The effectiveness of the system is demonstrated by the high detection rates and low latencies across various test cases, underscoring its utility in critical, real-time applications. , Claims:1. A deep learning-based image recognition system optimized for real-time object detection and process monitoring in dynamic environments, comprising:
a) a learning device including a neural processing unit (NPU), a graphical processing unit (GPU), and deep learning algorithms, configured to classify objects into types including moving objects, status objects, and vector objects;
b) a monitoring device comprising:
i. a multi-view video acquisition system with an array of cameras positioned to capture high-definition frames from multiple angles,
ii. an object detection module for analyzing captured frames to classify objects,
iii. a process classification module containing a database of stored process patterns for real-time pattern matching, and
iv. an abnormality detection unit for monitoring detected objects and identifying potential process abnormalities by calculating a loss value based on pre-defined thresholds; and
c) a communication interface facilitating continuous data transfer between the learning device and monitoring device, enabling real-time, adaptive monitoring.
2. The system as claimed in claim 1, wherein the learning device registers attributes of objects during an initial setup, storing them in a dedicated memory unit to support adaptive learning and feature refinement over time.
3. The system as claimed in claim 1, wherein the learning device employs a combination of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to facilitate spatial and temporal feature extraction for real-time classification.
4. The system as claimed in claim 1, wherein the multi-view video acquisition system operates under a central control unit, synchronizing frame capture across multiple cameras at a frame rate of at least 120 frames per second, providing lag-free, high-resolution data streaming.
5. The system as claimed in claim 1, wherein the object detection module includes a high-speed digital signal processor (DSP) to support parallel frame processing, enabling classification of objects in real time.
6. The system as claimed in claim 1, wherein the process classification module applies a similarity comparison algorithm selected from cosine similarity or dynamic time warping, which matches real-time feature patterns with stored patterns for accurate process classification.
7. The system as claimed in claim 1, wherein the abnormality detection unit includes a field-programmable gate array (FPGA) configured to support low-latency monitoring, generating alerts when detected object features deviate beyond specified thresholds.
8. The system as claimed in claim 1, further includes a feedback loop wherein the monitoring device communicates detected abnormalities to the learning device, triggering retraining of deep learning models to refine feature extraction and classification functions.
9. The system as claimed in claim 1, wherein the deep learning models on the learning device are updated periodically based on new data patterns, with the updated models deployed to the monitoring device to enhance real-time monitoring and classification capabilities.
10. The system as claimed in claim 1, wherein the software framework within the monitoring device supports multiple deep learning libraries, allowing integration of customized models tailored for specific process monitoring applications.

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