IoT Based on Potholes and Speed Breakers Detection and Alert System

Abstract: Amidst the rapid evolution of autonomous vehicle technologies, ensuring road safety remains a paramount challenge. Effective detection of lanes and potential hazards, including speed breakers and potholes, is critical for safe autonomous driving. In this study, we introduce an innovative Lane, Speed Breaker, and Pothole Detection System (LSPDS) utilizing YOLOv4 Tiny, a state-of-the-art object detection algorithm and computer vision techniques. Our system integrates computer vision and machine learning techniques for analysis of road conditions. By employing camera sensors, we capture the road scene and apply image processing algorithms to identify lanes, speed breakers, and potholes. Moreover, the system incorporates Firebase for user authentication and SMS services for real-time alerts. YOLOv4 Tiny is employed for accurate detection and classification of these features within the captured images, thereby enhancing the perception capabilities of autonomous vehicles.

With increasing road accidents due to improper and non-standard speed breakers, it is the need of the hour to address this issue appropriately, and due to this although speed breakers are built for safety, they are posing to be more of a danger. This is mainly due to building illegal speed breakers and not maintaining existing ones. The existing solutions are largely dependent on the user or the surrounding, both of which do not provide immediate accuracy and dependability. This paper presents a self-improving system with minimal user involvement and aims to cover nearly all the drawbacks of the current solutions. It suggests speed breaker detection by measuring the difference in the height between the road level and the vehicle. In this approach, GPS coordinates are stored in an online database system that is available to the public through a portal. When the vehicle is at a predefined distance away from the speed breaker, the user is notified resulting in improved accuracy with every usage.

Abstract: A large amount of energy is wasted by the vehicles on roads when they apply heavy breaks on speed breakers. This happens due to friction and a high level of energy is produced. Energy can be extracted from the vehicles speed and weight. Another major issue is of major accidents caused by potholes on Indian roads. It is really important to identify these potholes and notify the driver about them to avoid these fatal accidents. Our project is an attempt to solve both these issues an effective, innovative and feasible manner. The domains in our project are energy generation/conservation and image processing. To generate electricity, we want a fast-moving face that can generate electricity. The reciprocating motion of the reducer is converted into rotational motion thanks to the rack and pinion device and a special system called the cylinder. We have developed a smart speed reducer that passes from both sides of the car and generates electricity from there. This system is assembled using all particle board and connecting rods. The system uses a high-speed machine that uses shafts, rollers and electric motors to generate electricity. The roller mechanism is used to return the reducer to its original position. It converts rotational motion into linear motion, but in some cases we use it to convert linear motion into rotational motion. The mechanism is economical and easy to install. With proper planning, we can generate a lot of electricity from traffic. Now let's briefly look at the meaning of the hole. Ultrasonic sensors are used to identify holes and bumps and measure their depth and height respectively. This helps people avoid fatal accidents. These two ideas together we solve two problems that people face. Therefore, this project preserves the essence of design thinking

A mobile tracking application for emergency ambulance services is proposed in this paper with various challenges in vehicle tracking and monitoring system. The system issues alert messages in case of emergency. The alert system is essential to provide information about the location of the accident or driver's location. In the conventional method, the ambulance driver could not reach the accident location on time due to heavy traffic, leading to loss of many lives. With the current technological advancements in smartphones and mobile apps, a mobile app for tracking ambulance in real time is proposed in this paper. This application will let the ambulance driver record its availability and location. The user who executes the emergency will book the ambulance and log in to the user. The user location and nearby ambulances will be precisely pointed out on the Google map. Once the patient is boarded, the location of the ambulance will be recorded and sent to the traffic police present at the traffic signal. The driver then selects the nearby hospital on the map, which helps to treat the patients on time. The location of ambulance is tracked by the navigator based on Web Services. This technology will help the ambulance location to be updated in the database. In addition, the traffic signal can be controlled based on the upcoming ambulance vehicle thus providing a traffic less route.

The roadways in our country are continuously developing and expanding and many highways are in proximity to the forests, which increases the possibility of human-animal collision making deadly accidents imminent. This paper proposes an application that uses the ‘YOLO’ algorithm to efficiently recognize and classify the animals in the images that are passed to it as input and alert the user via the map interface along with the location of the animal on Google Maps. Deep Learning method is used here for animal detection and classification. Using Deep Learning, a detection and notification system is proposed. Our application trained for two datasets, tiger and elephant.

Speed is the one of the basic reasons for vehicle accident in India. To reducing the accidents the system is used that is `Accident Detection and Alert System'. This system alert when accident occurred and immediately contact to the emergency unit. Arduino UNO is processor of this system. It control the whole system. The input are Acceleration meter , Gyroscope, Vibration Sensor and output are LCD , Buzzer, GSM module, GPS module .When accident occurred their vibrations are detected by vibration sensor and accelerometer detects sudden changes in speed that may indicate an accident or crash. Gyroscope monitors changes in orientation or rotation of the vehicle, which can be useful for detecting rollovers or sudden swerving. GPS module provides real-time location data, which is useful for sending SMS of accident locations to emergency unit. GSM module get accident location and data send it to emergency unit. And Buzzer get ON and LCD display danger message

Quality health services are services that are provided effectively and efficiently, tailored to the needs and expectations of patients, but still in accordance with existing codes of ethics and standards, thus enabling the achievement of optimal levels of health. Health service institutions for the community, where every health facilities are required to always improve the quality of service in order to achieve the highest level of health. Every patient who has received service can provide their opinion through the Google Maps comments section which can describe the quality of service of the health facilities. The method used in this article is a literature review and the search for articles is carried out through 2 online databases: Google Scholar for national journals and PubMed for international journals. According to the reviewed literature, the rating given by health facility visitors affects the quality of the health facility. This is due to dissatisfaction with the health services received in the form of reviews with narratives describing feelings of annoyance, anger, and disappointment. Ratings on Google Maps provides real-time information about the quality of service at a health facility based on patient reviews.

Every year several road accidents take place solely credited to the potholes. In this paper, an Automatic Pothole Detection and Alert System is developed using ultrasonic sensor, accelerometer, stereo camera and Global Positioning System (GPS) integrated with Raspberry Pi. The system identifies a pothole, alerts the rider and generates location database of existing potholes. This in turn can create cautiousness in the riders, decrease number of accidents and maintenance costs of vehicles. The experimental results state that the system is able to identify the potholes with 90% accuracy. The cost of the system is around 8000 INR and it reduces average vehicle maintenance cost by 35-50 %. The time taken to send GPS location to the database and send alert to the rider is 4 sec. A strong, city-wise database is generated which can further chisel the system for the entire nation.

Motorcycle travel, deemed one of the riskiest modes of transportation, faces a staggering fatality rate, with 212.7 deaths for every million miles travelled. Unlike enclosed vehicles, motorcycles expose riders to their surroundings, heightening the need for proactive safety measures. This paper explores the development and implementation of a Motorcycle Crash Detection and Alert System (MCDAS) utilizing the Multi-axes accelerometer. The system is designed to detect when a motorcycle falls and promptly alert emergency services and contacts via Firebase cloud. The study delves into the challenges posed by two-wheeled vehicle dynamics and outlines a novel collision detection algorithm tailored for motorcycles. This algorithm is validated against experimental evidence, providing a robust foundation for crash detection systems in this unique context. Additionally, the paper introduces a novel method for vehicle detection and tracking, employing thresholding, mathematical morphology treatment, and original labelling to enhance accuracy and reduce artifacts. In the context of road accidents, especially prevalent among two-wheelers, the paper presents a system that combines accelerometer data and a user's heartbeat sensor to assess the severity of accidents. This timely information triggers alerts to nearby medical centers and contacts through GSM and GPS modules, facilitating immediate medical aid. The Android application associated with the system not only sends text messages to medical centers and friends but also shares the precise accident location, significantly reducing response time. important contributions and heralds a new departure for the vehicle safety paradigm.

The Community Crime Reporting and Alert System is a web-based application designed to facilitate real-time crime reporting and awareness through modern web technologies. Built using HTML, CSS, JavaScript (React.js or Vue.js for frontend), and Node.js with Express.js for backend, the system ensures a seamless user experience. It employs MongoDB or MySQL as the database to store user reports, authentication details, and crime statistics securely. The system supports user authentication (JWT or OAuth 2.0), allowing registered users to report crimes while also providing an option for anonymous submissions. Reported incidents include key details such as crime type, description, date, time, and location, which are stored in the database and visualized on an interactive Google Maps API integration.

Millions of people depend on the navigation facilities available in smart-phones and web browsers for their daily commutes, planning long trips ahead of time, looking up places etc. Integration of GPS and compass made navigating anywhere in the world a trivial task. Today, there are several applications available that fit the purpose of navigation such as Waze, HereWeGo (previously known as Here Maps by Nokia), Google Maps, etc. When Google Maps was used to embark on a tour that will take us to chosen places by covering the least distance possible, it is observed that none of the aforementioned applications provide such a feature. In this paper, a framework is developed with Google Maps APIs to create such a feature. This problem is mapped to the Traveling salesman problem and tried to solve it using algorithms known for approximating TSP such as Artificial Bee Colony Algorithm, Particle Swarm Optimization and Two-opt Algorithm. The framework is tested with these algorithms and found that, Particle Swarm Optimization gives the best possible route.

Association rule mining has recently attracted strong attention and proven to be a highly successful technique for extracting useful information from very large databases. In this paper, we explore a generalized affinity-based association mining which discovers quasi-equivalent media objects in a distributed information-providing environment consisting of a network of heterogeneous databases which could be relational databases, hierarchical databases, object-oriented databases, multimedia databases, etc. Online databases, consisting of millions of media objects, have been used in business management, government administration, scientific and engineering data management, and many other applications owing to the recent advances in high-speed communication networks and large-capacity storage devices. Because of the navigational characteristic, queries in such an information-providing environment tend to traverse equivalent media objects residing in different databases for the related data records. As the number of databases increases, query processing efficiency depends heavily on the capability to discover the equivalence relationships of the media objects from the network of databases. Theoretical terms along with an empirical study of real databases are presented.

In the era of the internet and with an expanding number of individuals as its end users, countless trespassers are presented every day. In 2016, four Pakistani terrorists illegally entered into the Indian army base and carried out grenade attacks on the security forces. Thus, tracking the movement and the location of different trespassers and intervention of unknown or unauthorized persons with the assistance of Intruder Detection Systems is the proposed work in this paper. Intruder Detection System is a developing pattern in research in today's technological world. Existing investigations and with their use of technology show adequacy and poor approaching ability of AI in dealing with Intruder Detection Systems. In this research paper, we intend to clarify and improvise the identification pace of Intruder Detection System by utilizing OpenCV a built-in library in python.KeywordsOpenCVImage thresholdingImage smoothingContours

Speed is one of the basic reasons for vehicle accident.Many lives could have been saved if emergency services could get accident information and reach in time.This project deals with accident detection system when an accident occurs it uses various components and alerts the Rescue team for help.An efficient automatic accident detection with an automatic notification to the emergency service with the accident location is a prime need to save the precious human life.The proposed system deals with accident alerting and detection.It reads the exact latitude and longitude of the vehicle involved in the accident and sends this information to nearest emergency service provider.The goal of the project is to detect accidents and alert the rescue team in time.

Effective pharmaceutical management is vital for healthcare systems, where timely identification of drug expiry dates is crucial to ensure patient safety and regulatory compliance. This paper presents an automated system for drug expiry date detection and email alerting, aimed at enhancing the efficiency and reliability of pharmaceutical management. Traditional manual tracking methods are often prone to errors and inefficiencies, leading to potential health risks. The proposed system automates this process, ensuring prompt notifications to relevant stakeholders. The system features a centralized database for storing drug information, barcode or QR code scanning for data entry, an expiry date detection algorithm, and an integrated email alerting mechanism. The database, managed using MySQL, contains detailed records of each drug, including its name, batch number, manufacturing date, expiry date, and quantity. Barcode or QR code scanners facilitate quick and accurate updates to the database, implemented through a Python-based application. The expiry date detection algorithm runs periodically, comparing current dates with stored expiry dates to identify drugs nearing expiration within a specified threshold, typically 30 days. Upon detection, the system triggers email alerts with detailed drug information sent to pharmacists, healthcare providers, and inventory managers, prompting timely action to prevent the use of expired medications. Testing in a controlled environment with a sample dataset demonstrated that the algorithm accurately identified drugs nearing expiry and generated appropriate email alerts. User feedback indicated significant improvements in efficiency and a reduced risk of expired drug usage. This automated system offers a proactive approach to managing drug inventories, minimizing human error, and enhancing compliance with health regulations. By automating expiry date tracking and alerting, it improves pharmaceutical safety and streamlines inventory management. Future enhancements could include additional notification methods, such as SMS alerts, and expanding the system’s capabilities to manage other aspects of pharmaceutical inventory.