Dental Caries Detection System Using Machine Learning and IOT

This research Paper proposes an application of Image Processing that works on the principle of machine learning and YOLO (You Only Look Once) algorithm was used to detect the custom object. The proposed application is a method to detect non-biodegradable waste from the bins so that the non-biodegradable waste can easily be separated from the bins. Data was divided into three major categories of non- biodegradable waste that were glass, metal and plastic. 450 -500 images of each category were collected in order to train the model. Each image was labelled using a labelling tool in the format of yolo. A machine learning model was created that was trained on the data set of all three categories of the images of non- biodegradable waste. After completion of training a number of files were obtained that were further used in testing model of the application. After completion of testing and checking the accuracy of the model on the webcam all the files were finally imported on the raspberry pi camera. Final testing was done on raspberry pi camera and results were verified accordingly and accuracy was calculated on the basis of the output received.

With the continuous advancement of industrial automation, product quality inspection has become increasingly important in the manufacturing process. Traditional inspection methods, which often rely on manual checks or simple machine vision techniques, suffer from low efficiency and insufficient accuracy. In recent years, deep learning technology, especially the YOLO (You Only Look Once) algorithm, has emerged as a prominent solution in the field of product defect detection due to its efficient real-time detection capabilities and excellent classification performance. This study aims to use the YOLO algorithm to detect and classify defects in product images. By constructing and training a YOLO model, we conducted experiments on multiple industrial product datasets. The results demonstrate that this method can achieve real-time detection while maintaining high detection accuracy, significantly improving the efficiency and accuracy of product quality inspection. This paper further analyzes the advantages and limitations of the YOLO algorithm in practical applications and explores future research directions.

- The integration of artificial intelligence (AI) into skincare and cosmetics has opened new opportunities for personalized beauty solutions.Introduces a chatbot system that provides tailored cosmetic recommendations based on individual skin types and conditions.Utilizing the YOLO (You Only Look Once) algorithm, the system performs real-time skin analysis by detecting skin types such as acne-prone-skin, burned-skin, dry-skin and oily-skin from user-provided images.The chatbot combines these insights with user input on preferences and concerns to deliver customized product suggestions and skincare routines.The YOLO algorithm's efficiency in object detection ensures rapid and accurate analysis, while the chatbot interface enhances user accessibility and interaction. Key Words: Facial recognition,Skin analysis,Cosmetic suggestions,Chatbot

To ensure the safe operation of highway traffic lines, given the imperfect feature extraction of existing road pit defect detection models and the practicability of detection equipment, this paper proposes a lightweight target detection algorithm with enhanced feature extraction based on the YOLO (You Only Look Once) algorithm. The BIFPN (Bidirectional Feature Pyramid Network) network structure is used for multi-scale feature fusion to enhance the feature extraction ability, and Varifocal Loss is used to optimize the sample imbalance problem, which improves the accuracy of road defect target detection. In the evaluation test of the model in the constructed PCD1 (Pavement Check Dataset) dataset, the mAP@.5 (mean Average Precision when IoU = 0.5) of the BV-YOLOv5S (BiFPN Varifocal Loss-YOLOv5S) model increased by 4.1%, 3%, and 0.9%, respectively, compared with the YOLOv3-tiny, YOLOv5S, and B-YOLOv5S (BiFPN-YOLOv5S; BV-YOLOv5S does not use the Improved Focal Loss function) models. Through the analysis and comparison of experimental results, it is proved that the proposed BV-YOLOv5S network model performs better and is more reliable in the detection of pavement defects and can meet the needs of road safety detection projects with high real-time and flexibility requirements.

Human wildlife conflict occurs when encounters between humans and wildlife result in negative outcomes such as loss of property, livelihood, and even life for both humans and wildlife, which has a significant impact on the eco system. Interactions between people and wild animals are becoming more common as the human population grows and biodiversity increases. Detecting wild animals and preventing them from entering human habitats, resulting in human-wildlife conflict. It is intended to use machine learning to identify various types of endangered animals. The Dataset contains over 10,000 images in which 7,000 images are training images and 3,000 images are testing images. Our proposed solution employs You only Look once (YOLO) algorithm which is a Convolution Neural Network (CNN) to detect and identify the wild animals using Machine Learning model. The proposed method yields 94% accuracy in detecting the endangered wild animals through the computer vision algorithms. The achieved accuracy percentage is comparatively the highest among the existing model [2] because of the YOLO V4 model.

With the help of today's developing technology; many applications such as traffic monitoring, target detection, empty parking lot assignment etc. have become applicable with machine learning and deep learning methods. These applications are aimed to determine the desired object from the photographs. In this study, it is aimed to develop an approach that able to identify the vehicles through aerial images by using the YOLO (You Only Look Once) algorithm, feeding it with a trained convolutional neural network structure. As a result of the study, an application that can detect vehicles was developed, increased the performance rate of the YOLO algorithm by 3.2%.

Advancements in digital imaging and machine learning have transformed healthcare, enabling innovative solutions for automated drug identification. This study develops an image-based system to classify pharmaceutical drugs, tackling errors arising from visual similarities in their shape, color, or size. Accurate drug identification is crucial for healthcare professionals and patients to access reliable information on drug composition, usage instructions, and potential side effects, enhancing safety and efficiency in medical practice. The system leverages the YOLO (You Only Look Once) algorithm, renowned for its speed and precision in object detection. A dataset comprising 5,000 drug images sourced from Kaggle was curated, with annotations and augmentation techniques such as horizontal flipping, rotation, and scaling to improve model robustness. The YOLOv11 model achieved a precision of 97.4%, a recall of 97.6%, and a mean average precision (mAP@50) of 98.4%, demonstrating high reliability in real-world scenarios. Integrated with a user-friendly Tkinter interface, the system facilitates real-time drug detection and information retrieval, streamlining access to critical data. This research underscores the YOLO algorithm’s effectiveness in delivering rapid and accurate drug classification, offering a scalable solution for healthcare applications. The system’s success highlights its potential to reduce medication errors and improve patient outcomes through precise and accessible drug identification technology.

There are new regulations requiring the use of masks or face shields to prevent the transmission of Covid-19. Using deep learning, a model can be made to detect faces that use masks and face shields by training the model using the previous pre-trained model and using a custom dataset. The purpose of this study is to create a deep learning model that can detect faces with and without masks and as well as face shields for the prevention of covid-19 transmission using YOLO (You Only Look Once) with pre-trained models and custom datasets in real-time. In this study, using pre-trained models from YOLOv3, YOLOv3-Tiny, YOLOv4, YOLOv4-Tiny, and YOLOv4-Tiny-3l with Darknet Framework and compare between average pooling and max pooling in the convolutional neural network YOLO to detect face masks and face shields as a real-time. From experiment the highest mAP was obtained from YOLOv4 using average pooling with a value is 97.64% although the difference is not too much with YOLOv4 using max pooling with value 97.57% and the lowest was YOLOv3-Tiny using max pooling, which was 94.09%, and for the highest FPS was obtained by YOLOv4-Tiny with Fps values is 171 and mAP 96.75%. And for real-time detection of face masks and face shields, the best model used in testing using webcam 1080p is from YOLOv4-Tiny, because the FPS is quite good and the mAP is quite high.

This research aimed to develop detection model for aflatoxin-contaminated cocoa beans using YOLO (You Only Look Once) method based on fluorescence imaging. Aflatoxin level measurement and image acquisition were carried out on cocoa beans inoculated with Aspergillus flavus (6 mL/100 g) and incubated for 7 days. The quantification of aflatoxin levels was achieved through Liquid Mass Chromatography (LCMS), which served as the basis for categorizing the images into 3 groups, namely aflatoxin-free, contaminated below and contaminated above the threshold. The image acquisition was conducted in a mini studio equipped with UV light. Furthermore, image annotation was carried out using Roboflow, while YOLOv5 was employed as detection model for aflatoxin-contaminated cocoa beans. The performance of YOLO model, based on testing data, yielded the following metrics: precision (all): 0.91, recall (all): 0.93, mean average precision (all): 0.95 and mAP@[.5:.95] (all): 0.47. The average accuracy based on the confusion matrix from the testing data was 91%. These results showed that a combination of YOLOv5 model and fluorescence imaging presented a highly performing alternative for detecting various aflatoxin contamination levels in cocoa beans.

Among the population of India, nearly 1.6 million children are blind or visually impaired by birth. The challenges faced by the visually impaired persons are high in day-to-day life. There are number of challenges faced by them like requiring guidance for the usage of public transport, to walk independently. One of the most common problems is the dependency on others for the purchase of household items. Hence, a unique solution is required which makes the people to identify and purchase the products in the supermarket without dependency. This research focuses on a portable camera-based assistance that helps them to identify the grocery items along with the framework to read the text on the labels. This experimental system consists of three modules: object detection and classification; reading the text from the label; text to audio converter. The input to the camera is fed by the Raspberry Pi kit. The captured video is segregated as pictures of the grocery items. The proposed deep learning algorithm for the detection of object is You only look once (YOLO). The accuracy of the detection is pre-dominantly high compared to the existing algorithms. The captured image of the grocery item label is preprocessed to blur the unwanted text. Optical character recognition (OCR) is applied over the label to convert as the text where machine can read. At last, the processed text is converted as audio for the guidance of the people. Thus, a flexible supervisory mechanism with user-friendliness is developed for helping the visually impaired people.KeywordsDeep learningVisually impairedSmart super market assistanceOptical character recognition

Real-time object detection using the YOLO (You Only Look Once) algorithm has shown promising performance in various computer vision applications. However, its application on devices with limited resources is still a challenge due to its high computational requirements. This study aims to optimize the YOLOv5 model for fire and smoke detection on Orange Pi Zero 3 devices using quantization techniques. Using a dataset of 2247 fire and smoke images, this study applies static quantization techniques to improve model efficiency. The methodology includes training of standard YOLOv5 models, conversion to ONNX format, and application of static quantization. Results show a significant improvement in computational efficiency, with a 42.2% reduction in model size and a 65.21% increase in inference speed. Despite a decrease in the mAP value by 25.6%, the optimized model was still able to perform object detection at a significantly higher speed. In conclusion, the quantization technique is effective in optimizing the YOLOv5 model for deployment on edge computing devices, despite the trade-off between speed and accuracy.

Shrimp farming activities can be divided into hatchery and rearing. During the hatchery process, there is an activity of counting shrimp larvae. Currently, the method of counting larvae was still done manually using the sampling method by taking a full cup of shrimp larvae and counting them manually. This process was timewasting and can make the result in miscalculations due to human error. Therefore, this study proposed the You Only Look Once (YOLOv3) algorithm to perform automatic counting of the quantity of shrimp larvae. The results of the YOLOv3 final model show a good performance with a mean Average Precision (mAP) value of 96.83% and an average accuracy value of 76.48%. This YOLO algorithm can calculate shrimp larvae with hight precision.

The novel coronavirus (COVID-19) has affected more than two million people of the world, and far social distancing and segregated lifestyle have to be adopted as a common solution in recent years. To solve the problem of sanitation control and epidemic prevention in public places, in this paper, an intelligent disinfection control system based on the STM32 single-chip microprocessor was designed to realize intelligent closed-loop disinfection in local public places such as public toilets. The proposed system comprises seven modules: image acquisition, spraying control, disinfectant liquid level control, access control, voice broadcast, system display, and data storage. The STM32 microcontroller is the main control chip and collects the disinfectant liquid level information and crowd density by using flow sensors, pressure and image. Single chip microcomputer enabling composite control of disinfectant concentration and liquid level through proportion integration differentiation (PID) control and logical increase/decrease ratio control. The use of the You Only Look Once (YOLO) algorithm aids in improving the accuracy of human target recognition, dynamically obtaining the crowd density, and regulating the spraying strategy. A comparison of the dynamic changes in crowd density with the user-defined crowd density threshold is performed to optimize the access control time and model parameters and obtain the optimal access control time limit. This approach enables dynamic optimization and intelligent control of the proposed full-cycle, closed-loop disinfection model for public toilets, effectively reducing the risk of virus transmission.

Real-time deep learning-based position control of a mobile robot

Object detection is a fundamental problem in computer vision, with applications spanning self-driving cars, surveillance systems, medical imaging, robotics, and smart cities. Among the myriad of algorithms developed for this task, the You Only Look Once (YOLO) family stands out for its ability to perform real-time and accurate object detection. This article provides a comprehensive analysis of the YOLO algorithm series, from YOLOv1 to YOLOv8, evaluating them across key performance metrics, including precision, recall, mean Average Precision (mAP), frames per second (FPS), and overall effectiveness. Unlike traditional two-stage detectors such as R-CNN, YOLO formulates object detection as a single regression problem: a single pass over the image simultaneously predicts bounding boxes and class probabilities. This end-to-end design enables YOLO to achieve high speed while maintaining a competitive accuracy-efficiency trade-off. We examine architectural innovations across YOLO versions, including batch normalization, anchor boxes, residual blocks, feature pyramid networks, and attention mechanisms, and discuss their impact on performance. Lightweight models (e.g., YOLOv5-Nano, YOLOv8-Small) are explored with a focus on their suitability for mobile and embedded systems, highlighting YOLO’s adaptability to resource-constrained environments. Challenges such as small object detection, occlusion, and domain-specific tuning are also addressed. This article serves as a practical guide for researchers, developers, and practitioners aiming to leverage YOLO for real-world object detection tasks.