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1. Next gen self driving safety with pothole detection via AI and
YOLO V8 deep learning
Presented By <Batch – Batch No.>
1. <Name><Roll No.> 3. <Name><Roll No.>
2. <Name><Roll No.> 4. <Name><Roll No.>
Under the guidance of:
<Faculty Name><Highest Qualification only>
<Academic Designation Only>

2. OUTLINE
1. Abstract
2. Introduction
3. Problem Definition
4. Objectives
5. Existing System
 Limitations / Disadvantages
6. Proposed System
 Proposed System Block Diagram
7. References

3. ABSTRACT
• Pothole detection plays a crucial role in ensuring road safety and maintenance. In
this study, we explore the application of YOLOv8, an efficient object detection
algorithm, for the automated identification of potholes in road imagery. We leverage
a curated dataset of diverse road scenarios, annotated with bounding boxes around
potholes.
• The YOLOv8 model is configured and trained on this dataset, incorporating
variations of the YOLO architecture to enhance performance. The training process
involves optimizing parameters such as image size, batch size, and epochs. The
resulting model demonstrates the ability to detect potholes in real-world images with
a high level of accuracy.
• To assess the model's performance, we employ standard evaluation metrics,
including precision, recall, and F1 score. Additionally, we utilize Intersection over
Union (IoU) as a measure of bounding box overlap. The model is tested on a
separate validation set to ensure its generalization capability.
• In the inference phase, the trained YOLOv8 model is applied to new road images,
providing real-time detection of potholes. Post-processing techniques, such as non-
maximum suppression, are implemented to refine and filter the detected bounding
boxes.

4. • Our evaluation results demonstrate the effectiveness of the YOLOv8 model in
accurately identifying and localizing potholes. The chosen metrics reveal a balanced
trade-off between precision and recall, showcasing the model's reliability in practical
scenarios. The proposed approach exhibits promising potential for integration into
smart city infrastructure, contributing to proactive road maintenance and improved
public safety.

5. INTRODUCTION
•
A pothole is a type of road defect or depression in the surface of a road caused
by the combination of traffic wear, weathering, and the effects of freezing and
thawing. Potholes can vary in size, shape, and depth, and they pose a
significant hazard to both drivers and pedestrians.
•Potholes can cause substantial damage to vehicles, including flat tires, bent
rims, and suspension damage.
•The economic impact of potholes extends beyond individual vehicle repairs. It
includes costs associated with emergency road repairs, increased fuel
consumption, and disruptions to transportation systems.
•The World Economic Forum reports that inadequate road infrastructure is a
significant impediment to economic growth and development, emphasizing the
importance of proactive maintenance.

6. PROBLEM DEFINITION
• Haar cascades or template matching, might struggle to
achieve both accuracy and real-time performance
simultaneously. Accurate pothole detection requires
capturing various shapes, sizes, and lighting conditions.
• Traditional CNN-based object detection approaches (e.g.,
R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN) use a
two-stage approach involving region proposal and
classification.
• CNN is Slower compared to YOLO, especially when
dealing with multiple region proposals. Multiple passes
through the network for region proposals and object
classification can be time-consuming.

7. OBJECTIVES
a. The primary objective is to enhance road safety by
developing an efficient pothole detection system.
b. Integrate the wandb (Weights & Biases) deep learning
platform to track model training progress and performance
metrics.
c. Creating a real-time prediction system for images and videos
with a Tkinter GUI

8. EXISTING SYSTEM
• Haar cascades or template matching
• R-CNN (Region-Based Convolutional Neural Networks)
• LeNet-5
• AlexNet
• EfficientNet
• MobileNet

9. LIMITATIONS
• Haar Cascades or Template Matching:
▫ Limitation: Limited to detecting simple patterns or features;
struggles with complex or varied object shapes, lighting conditions, and
scales.
• R-CNN (Region-Based Convolutional Neural Networks):
▫ Limitation: Time-consuming due to multiple regions of interest
extraction and classification; not suitable for real-time object detection.
• LeNet-5:
▫ Limitation: Designed for simpler tasks like handwritten digit
recognition; lacks depth and complexity needed for more intricate
object recognition tasks.
• AlexNet:
▫ Limitation: Depth is relatively shallow compared to modern
architectures; might not capture intricate features as effectively.
• EfficientNet:
▫ Limitation: Balance between efficiency and accuracy might not
always provide the highest performance in highly specialized tasks.
• MobileNet:
▫ Limitation: May sacrifice some accuracy due to its leading to trade-
offs between speed and precision.

10. PROPOSED SYSTEM
• Collect Data
• Import data into Roboflow Annotate.
• Open an image.
• Label data with bounding boxes or
polygons.
• Download annotated data
• Train the model using yolov8
• Visualize Log metrics with wandb during
model training or evaluation.
• Compare model predictions or results
• View, sort, filter and download final best
trained model
• Create GUI with TKINTER
• User image/video prediction

11. Software and Hardware
HARDWARE:
• Processor: Intel® Core™ i3-2350M CPU @ 2.30GHz
Installed memory (RAM):4.00GB
• System Type: 64-bit Operating System
• Hard disk: 10 GB of available space or more.
• Display: Dual XGA (1024 x 768) or higher resolution
monitors
• Operating system: Windows
SOFTWARE
• PYCHARM IDE
• ANACONDA
• ROBOFLOW ANNOTATION TOOL
• WANDB

12. REFERENCES
1. O. Mendoza, P. Melin and G. Licea, "A New Method for Edge Detection
in Image Processing using Interval Type-2 Fuzzy Logic," in 2007 IEEE
International Conference on Granular Computing, California, 2007.
2. T. Kim and S.-K. Ryu, "Review and analysis of pothole detection
methods," Journal of Emerging Trends in Computing and Information
Sciences, vol. 5, no. 8, pp. 603-608, 2014.
3. A. Bhat, P. Narkar, D. Shetty and D. Vyas, "Detection of Potholes using
Image Processing Techniques," IOSR Journal of Engineering, vol. 2, pp.
52-56, 2018.
4. M. Muslim, D. Sulistyaningrum and B. Setiyono, "Detection and
counting potholes using morphological method from road video," AIP
Conference Proceedings, vol. 2242, no. 1, pp. 3-11, 2020.
5. Z.-Q. Zhao, P. Zheng, S.-t. Xu and X. Wu, "Object Detection with Deep
Learning: A Review," IEEE Transactions on Neural Networks and
Learning Systems, vol. 30, no. 11, pp. 3212-3232, November 2019.
6. J. Świeżewski, "YOLO Algorithm and YOLO Object Detection: An
Introduction," 3 January 2020. [Online]. Available:
https://appsilon.com/object-detection-yolo-algorithm/.