POTHOLE DETECTION SYSTEM USING MODIFIED YOLOV8 ARCHITECTURE ON ZYNQ ZCU104 ULTRASCALE + SOC FOR REAL-TIME ROAD MONITORING

Abstract

[019] The present invention relates to a Pothole Detection System using Modified YOLOv8 Architecture on Zynq ZCU104 UltraScale+ SoC for Real-Time Road Monitoring. An object of the invention is to provide a real-time pothole detection system based on a modified YOLOv8 architecture, implemented on the Zynq ZCU104 UltraScale+ SoC platform. The system uses an optimized deep learning model to detect potholes under diverse conditions, leveraging FPGA parallel processing for low-latency inference. Key architectural elements include customized backbone, neck, and head layers, enabling multi-scale detection of potholes. Pruning and quantization techniques reduce model size and power requirements. This efficient, hardware-accelerated system enhances road safety by enabling timely maintenance actions through automated pothole detection and localization. Accompanied Drawing [FIG. 1]

Specification

Description:[012] 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.
[013] 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.
[014] 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.
1. Architecture of the YOLOv8 Model for Pothole Detection
[015] The proposed YOLOv8 model is customized for identifying road surface defects like potholes, featuring key architectural modifications to enhance detection accuracy and efficiency:
Input Layer:
1.Image Input: The input images, typically 640x640 pixels, contain road elements like vehicles, lanes, and potholes.
2.Normalization: Input images are normalized to a [0, 1] pixel range to ensure efficient convergence during training.
Backbone (CSP Network for Feature Extraction):
I.Focus Layer: Slices and concatenates input data to create detailed feature maps, aiding in detecting smaller, irregular pothole details.
II.Convolutional Layers (3x3): Extracts local features, focusing on shapes and edges like pothole contours.
III.CSP Bottleneck Layers: Applies multiple convolutions with residual connections, facilitating gradient flow and feature extraction from textures and patterns across conditions.
IV.Max Pooling/Downsampling: Reduces spatial resolution, emphasizing the most relevant image features while retaining pothole details.
Neck (FPN + PAN for Multi-Scale Feature Aggregation):
I.Feature Pyramid Network (FPN): Aggregates features across scales, enhancing detection of both large and small potholes.
II.Path Aggregation Network (PAN): Improves localization by refining feature maps, capturing critical details like irregular pothole edges.
III.Upsampling Layers: Resizes smaller feature maps for effective fusion with larger scales.
IV.Concatenation Layers: Combines feature maps from various stages to capture multi-scale information.

Head (Detection Layers for Bounding Boxes, Classes, and Scores):
I.Convolutional Layers (3x3 and 1x1): Applies final convolution operations to generate detection outputs, focusing on pothole identification.
II.Anchor Boxes: Predefined anchor boxes adjust based on pothole shapes and sizes, facilitating accurate localization.
III.Bounding Box Prediction: Determines pothole location via bounding box coordinates (x, y, width, height).
IV.Class Score Prediction: Outputs scores indicating pothole likelihood, ensuring accurate classification.
V.Objectness Score: Confidence metric verifying that the detected object is a pothole, reducing false positives.
Output:
Bounding Box Coordinates: Identifies the pothole's location on the road surface.
Class and Objectness Scores: Assesses the probability and confidence of pothole detection.
2. FPGA-Based Acceleration on Zynq ZCU104 UltraScale+ SoC
The Zynq ZCU104 FPGA platform accelerates the YOLOv8 model through parallel processing:
Parallel Processing: Compute-heavy tasks, such as convolutions, are handled by the FPGA's computational kernels, optimizing latency for real-time applications.
Hardware-Software Co-Design: ARM cores manage data processing and model control, while the FPGA performs core computations, ensuring efficient system operation.
Pruning and Quantization: These techniques are applied to reduce model size and power consumption, maximizing the platform's efficiency without compromising detection performance.
3. System Workflow for Real-Time Pothole Detection
The system workflow includes the following stages:
Data Acquisition: Images are captured via road-facing cameras, processed in real-time for pothole detection.
Pre-Processing: Images are normalized and resized as per model requirements.
Model Inference: The YOLOv8 model, running on the FPGA, processes the image data and outputs bounding boxes, class, and objectness scores for detected potholes.
Post-Processing: Detection results are filtered based on confidence scores, and bounding boxes are displayed, enabling accurate pothole localization.
Alert Generation: Based on detected potholes, alerts are generated for maintenance teams, ensuring timely repair actions.
[016] 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.
[017] 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.
[018] 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. A pothole detection system comprising a modified YOLOv8 model optimized for road surface analysis, deployed on a Zynq ZCU104 UltraScale+ SoC platform, wherein the YOLOv8 model is configured to detect potholes under diverse environmental conditions with high accuracy and efficiency.

2.The system as claimed in claim 1, wherein the YOLOv8 model includes: A backbone network with Focus, Convolutional, CSP Bottleneck, and Max Pooling layers to extract features relevant to pothole detection from road images.

3.The system as claimed in claim 1, wherein the neck component comprises: Feature Pyramid Network (FPN) and Path Aggregation Network (PAN) layers that aggregate multi-scale features for effective detection of potholes of varying sizes.

4.The system as claimed in claim 1, wherein the detection head comprises convolutional layers and anchor boxes configured to detect pothole locations, sizes, and classification with confidence scores.

5.A hardware-software co-design framework for real-time pothole detection, comprising:
6.An FPGA-accelerated YOLOv8 model on the Zynq ZCU104 SoC platform, wherein compute-heavy operations are handled by the FPGA, and ARM cores manage data handling and model control.

7.The system as claimed in claim 5, further comprising optimization techniques including pruning and quantization to reduce model size and power consumption without compromising detection accuracy.

8.A multi-scale detection method in the pothole detection system as claimed in claim 1, wherein the YOLOv8 model generates bounding box predictions at multiple scales, allowing detection of potholes with varying shapes and sizes in real-time road monitoring applications.

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