Route Intensity Tracker using Machine Learning and Database Management

International Research Journal of Engineering and Technology (IRJET)

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Route Intensity Tracker using Machine Learning and Database Management Ambati Maneesha1, Munukoti Likhit2 1 Dept. of Information Technology Army Institute of Technology, Maharashtra, India 2 Dept. of Computer Engineering Army Institute of Technology, Maharashtra, India

---------------------------------------------------------------------***--------------------------------------------------------------------speed bumps: those that are clearly marked with visible paint Abstract - Due to the lack of maintenance on aging roads

or markings and those that have no such markings. Detecting the former is a relatively simple task using image processing techniques, but identifying unmarked speed bumps can be exceptionally difficult, especially when they lack the distinctive yellow or white warning stripes. While the human visual system can recognize marked speed bumps from a distance, it faces significant challenges when it comes to identifying unmarked ones. Hence, we introduce an approach for the detection of unmarked speed bumps, a critical step in preventing accidents and ensuring a smoother driving experience.

and roads in maintenance and the insufficient use of proper measures to prevent accidents, there has been a significant rise in the number of road accidents. Furthermore, it is advantageous to consider an alternative route after receiving advance information about the road conditions. This study delves into the utilization of image processing, object recognition using machine learning for analyzing photos and proactively alerting automobile drivers to potential hazards.

To achieve this, we will employ various techniques, including image processing, image data cleaning methods, speed breaker detection methods, pothole detection algorithms, database management system. Additionally, we will focus on enhancing and enhancing the accuracy and efficiency of this application.

Potholes, being a form of road damage, act as important indicators of structural problems within asphalt roads. The precise detection of potholes is crucial for deciding on the right maintenance and rehabilitation approaches for asphalt pavements. Nonetheless, manual detection and assessment methods are both costly and time-intensive. As a result, numerous initiatives have been launched to create technology capable of automatically identifying and categorizing potholes, with the goal of improving survey efficiency and advancing pavement improvement initiatives.

Key Words: Potholes, Speed bumps, Road intensity, Machine Learning, Database management, Real time detection, Convolutional Neural Network.

1.INTRODUCTION India, the world's most populous country, boasts a rapidly growing economy. In the context of Indian road conditions, the development of an automated driver guidance system holds paramount significance. The detection of speedbreakers, potholes or roads in maintenance is crucial as numerous accidents occur due to the abrupt appearance of these road hazards.

1.1 Potholes detection Detecting and distinguishing between potholes and smooth, well-maintained roads is a crucial task with far-reaching implications for road safety and upkeep. Machine learning has emerged as a formidable tool in tackling this challenge, offering multiple avenues to accurately identify and differentiate these road conditions.

Without a doubt, the inadequate maintenance of roads and the sudden appearance of potholes are significant factors contributing to road accidents. They not only jeopardize safety but can also inflict substantial damage to a vehicle's wheels and structure, along with a potential of accidents happening and lives matters on it. This technology offers early warnings to drivers as they select a roadway to their destination about speed limiters or sudden changes in road conditions, prompting immediate alerts.

Convolutional Neural Networks (CNNs) have proven highly effective in this context. By training these neural networks on a dataset encompassing images of both pothole-laden and smooth roads, they can learn to identify distinguishing visual features. CNNs excel at recognizing patterns, textures, and shapes, making them adept at identifying potholes in images.

Table -1: Pothole detection Algorithms performances

Speed bumps are strategically positioned in areas where it's essential to control vehicle speed for accident prevention, such as accident-prone zones, sharp curves, congested residential streets, and unguarded level crossings. The physical characteristics of these road features, including their height, length, and the presence of ramps, can exhibit considerable variation. There are generally two types of

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Algorithm

Estimated Accuracy

Computer Vision (CNN)

Approximately 85-95%

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International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 10 Issue: 10 | Oct 2023

p-ISSN: 2395-0072

www.irjet.net

LiDAR-based

Approximately 90-98%

Acoustic Sensors

Approximately 80-90%

Machine Learning (Overall)

Approximately 88-96%

1.2 Speed-bump detection Detecting and telling the difference between marked and unmarked speed bumps on the road is important for road safety. Machine learning can help with this task, and there are different ways to do it.

One way is to use cameras and computer vision. This means using cameras to look at the road and then using computer programs to understand what the camera sees.

Convolutional Neural Networks (CNNs) are especially good at this. We can teach these neural networks to recognize speed bumps, especially those with lines or symbols painted on them. CNNs are good at recognizing shapes, patterns, and textures, so they can spot marked speed bumps.

However, among these diverse approaches, machine learning emerges as the superior choice for several compelling reasons. Firstly, machine learning models possess the capacity to adapt and refine their performance over time.

Secondly, machine learning models can grapple with complex and non-linear data relationships. Machine learning algorithms, especially deep learning models like CNNs, are adept at capturing these subtle differences and making generalizations based on them.

Table -2: Speed bump detection Algorithm performances

A strong image recognition system may be easily built and deployed by anyone thanks to the object detection API of Tensorflow. It offers a variety of pre-trained models (trained on various datasets) that, after being tweaked, can be used to create unique classifiers, detectors, and recognizers. We've chosen the "F-RCNN inception v2" model.

Transfer Learning applies the knowledge acquired while resolving one problem to another, unrelated one. We may make significant time savings with this method. In the technique below, we choose any pre-trained model and then refine it. To improve the pre-trained CNN, we get the new dataset. The same weights can be used to extract the features from the new dataset if it shares characteristics with the old dataset. The dataset in our instance differs significantly from the original dataset. In contrast to the later layers of CNN, which become increasingly more focused on the specifics of the classes in the original dataset, the earlier levels of CNN contain more general properties.

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Impact Factor value: 8.226

Estimated Accuracy

Computer Vision (CNN)

Approximately 8595%

LiDAR-based

Approximately 9097%

Acoustic Sensors

Approximately 8090%

Machine Learning (Overall)

Approximately 8896%

But why is machine learning the best choice? Here are some reasons:

The improved version of the inception network series is called inception v2. The architecture of CNNs is intricate. The first installment in this series is called Inception v1.

Convolution is performed on the input using Inception v1 using 3 distinct filter sizes (1x1, 3x3, and 5x5). Max pooling is additionally utilized. The following conception module receives the concatenated Outputs after being provided to it.

The number of input channels is limited by adding an additional 1x1 convolution before the 3x3 and 5x5 convolutions because deep learning networks are computationally expensive. The GoogLeNet neural network architecture, which has nine of these inception modules, was created using this inception module. Additionally, the Inception v2 architecture is introduced to speed up calculation.

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Algorithm

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a.

Combining Different Data: Machine learning can use information from cameras, LiDAR, and acoustic sensors all at once. This makes it better at telling the difference between marked and unmarked speed bumps.

b.

Handling Complexity: Roads and speed bump markings can be different in many ways. Machine learning is good at handling these differences and adapting to new situations.

c.

Improvement Over Time: Machine learning models can get better as they see more data and as road conditions change. This is important because roads can change due to weather or repairs.

d.

Real-Time Processing: Machine learning can work in real-time, so it can give drivers warnings or adjust a vehicle's suspension right away when it detects a speed bump. This helps with safety and comfort.

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e.

International Research Journal of Engineering and Technology (IRJET)

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Volume: 10 Issue: 10 | Oct 2023

p-ISSN: 2395-0072

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Scalability: Machine learning can be used on many vehicles and roads without costing too much. It's a cost-effective way to monitor roads.

But it's not just about where you're going; it's also about how safe the journey is. That's where the data about speed bumps and potholes comes in. These road issues can be dangerous for drivers, passengers, and vehicles. So, it's essential to know where they are and how bad they are. The database keeps this information, and it can be used by people in charge of roads and those who make navigation apps to take action to keep roads safe and in good shape.

Before we can use the CNN for our classification application, it must be trained. During the training phase, we give the CNN a variety of images along with the expected results or classes.

In order to create an optimum network, this process is repeated a certain number of times. To shorten the overall training period, we applied the transfer learning strategy. In transfer learning, the final classification layer of the pretrained convolution neural network is changed. These networks divide the data into 1000 or more classes after being trained on millions of photos over several weeks. We use the networks as-is and construct our network on top of them because they are so well-optimized.

Fig-4: Front end parameters for input The database also lets you look up information about speed bumps and potholes. This means you can find out how often they appear on certain routes. This is super helpful for city planners, road maintenance teams, and safety organizations.

It helps them figure out which roads need fixing and where they should focus their efforts. This way, they can use their resources wisely and make the roads safer for everyone.

Fig-3: Schematic representation of CNN Another way is to use LiDAR, which is like a laser scanner.

LiDAR sends out laser beams to make 3D maps of the road.

Machine learning can analyze these maps to find places where the road is uneven, like where there are speed bumps.

LiDAR may not see markings, but it can still tell us where the speed bumps are.

Additionally, the database can help with studying trends and patterns. By collecting data over time and in different areas, we can learn more about where and when speed bumps and potholes tend to show up. This information can guide decisions on how to improve roads and reduce hazards.

Acoustic sensors are another option. These sensors listen to the sounds that cars make when they drive over different road surfaces. Machine learning can learn the sounds of cars going over speed bumps and use that to detect them. Acoustic sensors can help, especially when it's hard to see speed bumps.

Ultimately, this database system is like a smart assistant for road information. It keeps track of where you're going, notes any road issues like speed bumps and potholes, and lets you find out how often these problems occur. It helps city planners, road maintenance teams, and safety groups make better decisions about road improvements, ultimately making our roads safer and more efficient.

1.3 Database Management System The proposed database system has an important job: it helps collect and store essential information about roads. It focuses on where a journey starts, where it ends, and three points in between. Plus, it also keeps track of things like speed bumps and potholes. This system is a big step forward in how we manage and keep our roads safe.

2. Methodology Popular for finding objects in photos is the object detection method YOLO. In order to forecast the vector of the bounding boxes and potholes, this technique uses a single neural network. It operates by dividing images into a 5x5-sized grid.

Each grid cell can forecast the number of potential bounding boxes and the degree of confidence (i.e., confidence score) that the cell will contain the object, in this case a pothole. We will get 5x5xN boxes as a result. The algorithm continues to eliminate the boxes that are below a certain minimum probability level because the majority of these boxes will have a very low likelihood.

The system is really good at keeping track of where you're going. It stores the starting point and ending point of a trip, as well as three places you might stop along the way. This makes it easier to plan routes for things like transportation services, emergency responses, and city planning. With this database, you can quickly find out the order of these places and how far apart they are, which helps with making smart decisions about travel routes.

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Models such as Convolutional Neural Networks (CNNs) and other deep learning algorithms are especially adept at identifying patterns and irregularities within visual data.

When these models are trained using a dataset that includes images of roads featuring potholes and speed bumps, they can effectively learn to identify the unique characteristics and shapes associated with these road conditions. This level of precision is challenging to achieve using traditional computer vision methods.

Machine learning also excels in real-time processing, a critical capability when it comes to pothole and speed bump detection. These road hazards can emerge suddenly, posing immediate risks to drivers. Machine learning models are capable of analyzing data and making decisions in real-time, enabling swift response mechanisms like warning signals, adaptive suspension systems, or instant alerts to road maintenance teams. Additionally, machine learning is costeffective and scalable. Once developed and trained, machine learning models can be deployed across a broad spectrum of vehicles and road infrastructure without incurring substantial additional costs. This scalability makes it feasible to establish comprehensive road monitoring systems, providing a cost-efficient solution suitable for both urban and rural areas.

Fig-5: YOLO Algorithm To eliminate all of the duplicate objects, the remaining bounding boxes are then shifted towards a non-max suppression. The YOLO V3 model used in this study is trained using entire photos and the probability of the class within bounding boxes. In comparison to the original approaches for object detection, this method has many advantages. The YOLO V3 model moves quickly. Because YOLO V3 works on object detection as a regression problem, a complicated pipeline is not required. This suggests that real-time video processing is also possible with latency as low as 25ms.

Before detecting and making predictions, YOLO V3 examines the image as a whole

Fig -7: Flow chart of the system Fig-6: Algorithm performance

While alternative methods such as traditional computer vision or manual inspections have been used for pothole and speed bump detection, they often fall short in terms of accuracy, efficiency, and scalability. Traditional computer vision techniques may struggle to cope with variable road conditions and lighting, whereas manual inspections are labor-intensive and susceptible to human error.

Machine learning has emerged as the top most effective methodology for detecting potholes and speed bumps due to its adaptability, precision, and versatility. Potholes and speed bumps present significant challenges in terms of road safety and infrastructure maintenance, and machine learning offers a multitude of advantages in addressing these issues.

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3. CONCLUSION

Lanes, Potholes and recognize road Signs in Indian Roads”, International Journal of Modeling and Optimization, Vol. 2, No.6, December 2012.

The main reasons for accidents are going too fast and hitting potholes. To prevent accidents, it's really important that the car can automatically see and understand speed limit signs and potholes. One of the hardest parts of making cars smarter about this is recognizing speed bumps and potholes that don't have any markings. Also, it's important to check if the methods we use to find speed bumps and potholes are working well. Researchers have come up with ways to measure how good the computer programs are at finding them. But there's still a lot of work to do to make these programs even better, especially when they have to look at tricky or messy pictures and find potholes and speed bumps really quickly. Overall, making these detection methods better will help us use computers to understand pictures and videos in all sorts of ways, like recognizing objects, separating parts of pictures, and following things as they move. This can be useful in fields like medicine, security, and self-driving cars.

REFERENCES [1]

M. Jain, A. P. Singh, S. Bali, and S. Kaul, (2012) Speedbreaker early warning system, in Proc. 6th USENIX/ACM Workshop Netw. Syst. Develop. Regions, Boston, MA, USA, pp. 1–6

[3]

M. Afrin, M. R. Mahmud, and M. A. C. Fernández et al., “Free space and speed humps detection using lidar and vision for urban autonomous navigation,” in proceedings IEEE Inteligent Vehicle Symposium, 3-7 June 2012, pp. 698–703.

[4]

Varmaa V. S. K. P et al, "Real Time Detection of Speed Hump/Bump and Distance Estimation with Deep Learning using GPU and ZED Stereo Camera", 8th International Conference on Advances in Computing and Communication (ICACC), 2018.

[5]

S Neinaber, MJ Booysen, RS Kroon, “Detecting potholes using Simple Image processing Techniques and real world Footage” Stellenbosch University, Private Bag X1, Matieland

[6]

Mae M. Garcillanosa, Jian Mikee L. Pacheco, Rowie E. Reyes, Junelle Joy P. San Juan “Smart Detection and Reporting of Potholes via Image-Processing using Raspberry-Pi Microcontroller” Cabuyao City, Philippines

[7]

Jin Lin and Yayu Liu Ajit Danti, Jyoti Y. Kulkarni, and P. S.Hiremath, “An Image Processing Approach to Detect

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Vijayalakshmi B, Kiran P, Kishor Jadav B ”Detection of Potholes using Machine Learning and Image Processing”, International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 NCAIT - 2020 Conference Proceedings

[9]

Rasyid, Alfandino; Yudianto, Firman; Wicaksono, Hendro; Ulil Albaab, Mochammad Rifki; Fajrul Falah, Muhammad; Fridelin Panduman, Yohanes Yohanie; Arman Yusuf, Alviansyah; Basuki, Dwi Kurnia; Tjahjono, Anang; Nourma Budiarti, Rizqi Putri; Sukaridhoto, Sritrusta (2019). [IEEE 2019 International Electronics Symposium (IES) - Surabaya, Indonesia (2019.9.27-2019.9.28)] 2019 International Electronics Symposium (IES) - Pothole Visual Detection using Machine Learning Method integrated with Internet of Thing Video Streaming Platform. , (), 672– 675.

[10] Dewangan,

Deepak Kumar; Sahu, Satya Prakash (2020). Deep Learning Based Speed Bump Detection Model for Intelligent Vehicle System using Raspberry Pi. IEEE Sensors Journal

Arunpriyan j, Sajith Variyar V.V, Soman K.P, Adarsh S, "Real-Time Speed Bump Detection Using Image Segmentation for Autonomous Vehicle", ICICCS 2019: Intelligent Computing, Information and Control System, pp. 308-315, 2019.

[2]

[8]

[11] Shah, Sandeep; Deshmukh, Chandrakant (2019). [IEEE

2019 IEEE Transportation Electrification Conference (ITEC-India) - Bengaluru, India (2019.12.172019.12.19)] 2019 IEEE Transportation Electrification Conference (ITEC-India) - Pothole and Bump detection using Convolution Neural Networks

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