Object Detection Classification Research Articles

REAL-TIME MONITORING FOR DETECTING LAKE POLLUTION AND BIOTIC CONSERVATION

This research unveils a comprehensive system designed to tackle plastic pollution in lakes autonomously, eliminating the necessity for human intervention. By harnessing sensor data and camera imagery processed through the YOLO algorithm, the system identifies plastic debris. It then calculates the debris density and compares it against a preset threshold. Once the threshold is exceeded, an automated email alert containing the density data is sent to relevant authorities. Additionally, water quality sensors are integrated to continuously monitor environmental conditions. Regular updates are provided to enable proactive measures in pollution prevention. This endeavor showcases the utilization of advanced technology to address environmental challenges and safeguard aquatic ecosystems' health. By employing automated detection and monitoring mechanisms, the system offers a sustainable approach to combat plastic pollution in lakes, fostering environmental conservation endeavors.

A Methodology for Estimating the Assembly Position of the Process Based on YOLO and Regression of Operator Hand Position and Time Information

These days, many assembly lines are becoming automated, leading to a trend of decreasing defect rates. However, in assembly lines that have opted for partial automation due to high cost of construction, defects still occur. The cause of defects are that the location of the work instructions and the work field are different, which is inefficient and some workers who are familiar with the process tend not to follow the work instructions. As a solution to establishing a system for object detection without disrupting the existing assembly lines, we decided to use wearable devices. As a result, it is possible to solve the problem of spatial constraints and save costs. We adopted the YOLO algorithm for object detection, an image recognition model that stands for “You Only Look Once”. Unlike R-CNN or Fast R-CNN, YOLO predicts images with a single network, making it up to 1000 times faster. The detection point was determined based on whether the pin was fastened after the worker’s hand appeared and disappeared. For the test, 1000 field data were used and the object-detection performance, mAP, was 35%. The trained model was analyzed using seven regression algorithms, among which Xgboost was the most excellent, with a result of 0.15. Distributing labeling and class-specific data equally is expected to enable the implementation of a better model. Based on this approach, the algorithm is considered to be an efficient algorithm that can be used in work fields.

Research on Identification and Location of Mining Landslide in Mining Area Based on Improved YOLO Algorithm

The wide range and high intensity of landslides in the mining area pose a great threat to the safety of human life and property. It is particularly important to identify and monitor them. However, due to the serious surface damage, small landslide scale, complex background and other factors in the mining area, it is impossible to accurately identify and detect the landslide in the mining area. It is necessary to select an efficient detection model to detect it. In this paper, aiming at the problem of landslide identification in mining area, the remote sensing image of mining area is obtained by unmanned aerial vehicle (UAV), and the landslide data set of mining area is constructed by data enhancement method. An improved YOLOv8 algorithm is proposed. By adding a mixed attention mechanism in the channel and spatial dimensions, the detection accuracy of the model for mining landslide is improved, and the monitoring of landslide changes in the mining area is successfully completed. At the same time, an algorithm for locating the landslide position is proposed. Through this algorithm, the detected landslide pixel coordinates can be converted into geodetic coordinates. The results show that the improved YOLOv8 algorithm proposed in this paper has a recognition accuracy of 93.10% for mining area landslides. Compared with the mAP@0.5 of the original YOLOv8 algorithm and YOLOv5 algorithm, the improved YOLOv8 algorithm has an increase of 4.2% and 5.1%. This study has realized the monitoring and positioning of the landslide in the mining area, which can provide the necessary data support for the ecological restoration on mining area.

Empowering Structural Integrity: YOLO-Based Crack Detection and ML-Driven Concrete Strength Prediction of Critical Infrastructure Caused due to Mining Operation.

In this paper, YOLO algorithm is used for detecting crack by incorporating machine learning approach utilized for predicting concrete strength and possible crack in critical infrastructure caused during mining operation. Over time, those fissures become more noticeable until they finally collapse. Big data is accumulating huge amount of data by utilizing remote sensing, earth penetrating RADAR, LiDAR, GPS, and satellite image data for large-scale operations. The performance of the proposed approach is compared with other state-of-the-art algorithms, such as, YOLO V3 and YOLO V8. To investigate the performance, four performances metric like mean absolute error (MAE), root mean square error (RMSE), relative standard error (RSE) and root relative squared error (RRSE) has considered. It is found that our approach is giving superior results in term of the compared performance metrics. Moreover, non-parametric statistical testing, such as, signed test, Wilcoxon Rank test and Friedman's test has been conducted to verify the dominance of proposed approach over others.

INVOICE DATA EXTRACTION USING OCR TECHNIQUE

Traditional invoice processing involves manual entry of data, leading to human errors, delays,and increased operational costs. The lack of automation results in inefficiencies, hindering organizations from promptly accessing critical financial information. This research addresses the pressing need for a reliable OCR-based solution to automate invoice data extraction, ultimately improving accuracy, reducing processing time, and enhancing overall business productivity. The project aims to automate invoice data extraction through Optical Character Recognition (OCR) techniques. Leveraging advanced image processing and machine learning, the system will analyze scanned or photographed invoices, extracting relevant information such as vendor details, itemized costs, and dates.This automation streamlines manual data entry processes, enhancing accuracy and efficiency in managing financial records. OCR invoicing is the process of training a template-based OCR model for specific invoice layouts, setting up input paths for these invoices, extracting data, and integrating the extracted data with a structured database. Keywords: Invoice, OCR, YOLO algorithm, Data Extraction, Image Processing, Database Integration.

Leveraging Mobile Net and Yolo Algorithm for Enhanced Perception in Autonomous Driving

This project presents a cutting-edge autonomous navigation system designed to enhance spatial understanding and object recognition for vehicles. Equipped with cameras on both the left and right sides, by capturing comprehensive view of the surroundings, utilizing stereo vision for crucial depth information. The integration of Mobile Net and YOLO (You Only Look Once) algorithms is central to achieving real-time object detection and recognition. Mobile Net is employed for efficient feature extraction, ensuring optimal computational efficiency. Simultaneously, YOLO plays a pivotal role in rapid and accurate identification of objects within the captured images, contributing to the robustness of spatial understanding crucial for autonomous navigation. The result is a comprehensive and reliable autonomous navigation system, showcasing the effectiveness of combining cutting-edge technologies for improved real time decision making.

Application of Convolutional Neural Networks in Weed Detection and Identification: A Systematic Review

Weeds are unwanted and invasive plants that proliferate and compete for resources such as space, water, nutrients, and sunlight, affecting the quality and productivity of the desired crops. Weed detection is crucial for the application of precision agriculture methods and for this purpose machine learning techniques can be used, specifically convolutional neural networks (CNN). This study focuses on the search for CNN architectures used to detect and identify weeds in different crops; 61 articles applying CNN architectures were analyzed during the last five years (2019–2023). The results show the used of different devices to acquire the images for training, such as digital cameras, smartphones, and drone cameras. Additionally, the YOLO family and algorithms are the most widely adopted architectures, followed by VGG, ResNet, Faster R-CNN, AlexNet, and MobileNet, respectively. This study provides an update on CNNs that will serve as a starting point for researchers wishing to implement these weed detection and identification techniques.

Human-Following Strategy for Orchard Mobile Robot Based on the KCF-YOLO Algorithm

Autonomous mobile robots play a vital role in the mechanized production of orchards, where human-following is a crucial collaborative function. In unstructured orchard environments, obstacles often obscure the path, and personnel may overlap, leading to significant disruptions to human-following. This paper introduces the KCF-YOLO fusion visual tracking method to ensure stable tracking in interference environments. The YOLO algorithm provides the main framework, and the KCF algorithm intervenes in assistant tracking. A three-dimensional binocular-vision reconstruction method was used to acquire personnel positions, achieving stabilized visual tracking in disturbed environments. The robot was guided by fitting the personnel’s trajectory using an unscented Kalman filter algorithm. The experimental results show that, with 30 trials in multi-person scenarios, the average tracking success rate is 96.66%, with an average frame rate of 8 FPS. Additionally, the mobile robot is capable of maintaining a stable following speed with the target individuals. Across three human-following experiments, the horizontal offset Error Y does not exceed 1.03 m. The proposed KCF-YOLO tracking method significantly bolsters the stability and robustness of the mobile robot for human-following in intricate orchard scenarios, offering an effective solution for tracking tasks.

Surround Sensing Technique for Trucks Based on Multi-Features and Improved Yolov5 Algorithm.

The traditional rearview mirror method cannot fully guarantee safety when driving trucks. RGB and infrared images collected by cameras are used for registration and recognition, so as to achieve the perception of surroundings and ensure safe driving. The traditional scale-invariant feature transform (SIFT) algorithm has a mismatching rate, and the YOLO algorithm has an optimization space in feature extraction. To address these issues, this paper proposes a truck surround sensing technique based on multi-features and an improved YOLOv5 algorithm. Firstly, the edge corner points and infrared features of the preset target region are extracted, and then a feature point set containing the improved SIFT algorithm is generated for registration. Finally, the YOLOv5 algorithm is improved by fusing infrared features and introducing a composite prediction mechanism at the prediction end. The simulation results show that, on average, the image stitching accuracy is improved by 17%, the time is reduced by 89%, and the target recognition accuracy is improved by 2.86%. The experimental results show that this method can effectively perceive the surroundings of trucks, accurately identify targets, and reduce the missed alarm rate and false alarm rate.

Smart solutions for capsicum Harvesting: Unleashing the power of YOLO for Detection, Segmentation, growth stage Classification, Counting, and real-time mobile identification

This research paper explores a comprehensive approach to advancing capsicum harvesting by integrating cutting-edge technologies. The study addresses key objectives, including capsicum detection using various YOLO algorithms, peduncle detection through YOLO segmentation models in a proposed robotic harvester, laboratory testing of cutting target point coordinates using the Real Sense D455 RGB-D camera, growth stage determination, and capsicum counting/tracking with a supervision algorithm. The investigation highlights the YOLOv8s model as the most successful for capsicum detection, achieving a remarkable mean Average Precision (mAP) of 0.967 at a 0.5 Intersection over Union (IOU) threshold. As part of the growth stage determination task, YOLOv8s achieved a satisfactory mAP of 0.614 at the same IOU threshold. Additionally, the YOLOv8s-seg model demonstrated superior performance in peduncle detection, attaining a box mAP of 0.790 and a mask mAP of 0.771. The YOLOv8s-seg model excels in peduncle detection with a box mAP of 0.790 and a mask mAP of 0.771. Laboratory experiments using the Real Sense D455 RGB-D camera showcased its capability to localize the target point with a maximum error of 8 mm longitudinally, 9 mm vertically, and 12 mm laterally. The developed tracking and counting algorithm achieve a notable counting accuracy of 94.1 % during the third harvesting cycle in the greenhouse. The Android application developed demonstrated robust performance, achieving high accuracy (Mean IoU: 0.92), precise localization (Mean Euclidean Distance: 5 pixels), responsive user interface (Touch Response Time: 150 ms), and broad compatibility across Android versions and device types, with effective error handling (Success Rate: 95 %). The study not only advances capsicum harvesting techniques but also presents practical insights for the integration of advanced technologies, paving the way for efficient robotic harvesting systems in agriculture.

Water surface garbage detection based on lightweight YOLOv5

With the development of deep learning technology, researchers are increasingly paying attention to how to efficiently salvage surface garbage. Since the 1980s, the development of plastic products and economic growth has led to the accumulation of a large amount of garbage in rivers. Due to the large amount of garbage and the high risk of surface operations, the efficiency of manual garbage retrieval will be greatly reduced. Among existing methods, using YOLO algorithm to detect target objects is the most popular. Compared to traditional detection algorithms, YOLO algorithm not only has higher accuracy, but also is more lightweight. This article presents a lightweight YOLOv5 water surface garbage detection algorithm suitable for deployment on unmanned ships. This article has been validated on the Orca dataset, experimental results showed that the detection speed of the improved YOLOv5 increased by 4.3%, mAP value reached 84.9%, precision reached 88.7%, the parameter quantity only accounts for 12% of the original data. Compared with the original algorithm, the improved algorithm not only has higher accuracy, but also can be applied to more hardware devices due to its lighter weight.

OPTIMAL TRAFFIC CONTROL SYSTEM FOR TRAFFIC CONGESTION

A smart city's traffic management system is regarded as one of its primary components. Traffic jams are a common sight on the roadways in metropolitan areas due to the rapid increase in population and urban mobility. In order to address road traffic management issues and assist authorities with appropriate planning, an intelligent traffic management system utilizing the Yolo algorithm and Open CV approach is proposed in this project. A workable model for counting automobiles in traffic was developed using image processing as the basis. image processing methods classified and tallied moving vehicles in traffic scene video streams captured by stationary cameras. The following is the detection and tracking methodology. The adaptive background subtraction technique is initially used to separate the moving vehicles from the traffic scene. Using videos to isolated picture blobs are recognized as individual vehicles once the background is subtracted. Following blob identification, vehicles in a certain area are counted and classified. A count of vehicles was observed with an accuracy of ideal camera calibration. To support the goal, data is gathered from video footage of vehicles traveling toward and away from the camera in order to count and use signal switching. The created system's results demonstrate that, with more enhancements, it applicable to count and categorize vehicles in real-time. After that, an optimization framework makes use of these predictions to dynamically modify signal timings in response to shifting traffic conditions. The optimization method seeks to increase overall traffic flow efficiency, decrease delays, and shorten travel times. To sum up, utilizing real-time traffic data to optimize signal control presents a viable approach to improving urban traffic management. This method makes use yolo techniques to facilitate the creation of flexible and effective traffic control, which in turn helps to create more sustainable and seamless urban transportation networks. KEYWORDS: Image classification, Video tracking, Information analysis, Vehicle detection, Signal Switching.

Detection method of the seat belt for workers at height based on UAV image and YOLO algorithm

In the domain of outdoor construction within the power industry, working at significant heights is common, requiring stringent safety measures. Workers are mandated to wear hard hats and secure themselves with seat belts to prevent potential falls, ensuring their safety and reducing the risk of injuries. Detecting seat belt usage holds immense significance in safety inspections within the power industry. This study introduces detection method of the seat belt for workers at height based on UAV Image and YOLO Algorithm. The YOLOv5 approach involves integrating CSPNet into the Darknet53 backbone, incorporating the Focus layer into CSP-Darknet53, replacing the SPPF block in the SPP model, and implementing the CSPNet strategy in the PANet model. Experimental results demonstrate that the YOLOv5 algorithm achieves an elevated average accuracy of 99.2%, surpassing benchmarks set by FastRcnn, SSD, YOLOX-m, and YOLOv7. It also demonstrates superior adaptability in scenarios involving smaller objects, validated using a UAV-collected dataset of seat belt images. These findings confirm the algorithm's compliance with performance criteria for seat belt detection at power construction sites, making a significant contribution to enhancing safety measures within the power industry's construction practices.

Artificial Intelligence Based Virtual Assistant for Vision Impaired Person

Abstract: This project is a brilliant example of how technology can be harnessed for a profoundly positive purpose, enhancing the quality of life for those who may face challenges in certain aspects of daily living. It's about creating a tool that complements and supports individuals rather than substituting human interaction or assistance. That sounds like an incredible initiative! Combining technologies like object detection through YOLO algorithms, speech synthesis via text-to-speech, and incorporating them into smart glasses could significantly enhance the independence and mobility of visually impaired individuals. These glasses could provide real-time information about their surroundings, enabling them to navigate and interact more confidently with their environment.

Comparison Study of Corn Leaf Disease Detection based on Deep Learning YOLO-v5 and YOLO-v8

Corn is one of the primary carbohydrate-rich food commodities in Southeast Asian countries, among which Indonesia. Corn production is highly dependent on the health of the corn plant. Infected plants will decrease corn plant productivity. Usually, corn farmers use conventional methods to control diseases in corn plants. Still, these methods are not effective and efficient because they require a long time and a lot of human labor. Deep learning-based plant disease detection has recently been used for early disease detection in agriculture. In this work, we used convolutional neural network algorithms, namely YOLO-v5 and YOLO-v8, to detect infected corn leaves in the public data set called ‘Corn Leaf Infection Data set’ from the Kaggle repository. We compared the mean average precision (mAP) of mAP 50 and mAP 50-95 between YOLO-v5 and YOLO-v8. YOLO-v8 showed better accuracy at an mAP 50 of 0.965 and an mAP 50-95 of 0.727. YOLO-v8 also showed a higher detection number of 12 detections than YOLO-v5 at 11 detections. Both YOLO algorithms required about 2.49 to 3.75 hours to detect the infected corn leaves. This all-trained model could be an effective solution for early disease detection in future corn plantations.

Improved small-object detection using YOLOv8: A comparative study

In the last decade or so, deep neural networks have evolved at a rapid pace, where computer vision has been constantly refreshing its best performance and has been integrated into our lives. In the field of target detection, YOLO model is a popular real-time target detection algorithm model that is fast, efficient, and accurate. This research aims to optimize the latest YOLOv8 model to improve its detection of small objects and compare it with another different version of YOLO models. To achieve this goal, we used the classical deep learning algorithm YOLOv8 as a benchmark and made several improvements and optimizations. We optimized the definition of the detection head, narrowed its perceptual field, and increased its number, allowing the model to better focus on the detailed information of small objects. We compared the optimized YOLOv8 model with other classical YOLO models, including YOLOv3 and YOLOv5n. The experimental results show that our optimized model improves small object detection with higher accuracy. This research provides an effective solution for small object detection with good application prospects. With the continuous development and improvement of the technology, we believe that the YOLO algorithm will continue to play an essential role in object detection and provide a reliable solution for various real-time applications.

Remote Rescue Vehicle

Search and rescue operations in collapsed buildings provide crucial challenges due to the complexity of the environment and the need for information about trapped individuals. This document presents a versatile system equipped with various sensors, including LiDAR, temperature, GPS, GSM, and Wi-Fi modules, designed to assist rescue teams. The system data collection capabilities include high-quality video, audio, and image formats that supplement situational awareness. The LiDAR sensor is used to create detailed 3D models and identify potential hazards and obstacles. The thermal sensor helps in the detection of temperature signatures and enables the identification of surviving or overheated machines. Additionally, the integration of the YOLO algorithm enables real-time object classification, providing critical insights for faster decision-making. The GSM module enables communication with the system. The Wi-Fi module enables high-speed data transmission to ensure that the collected information is quickly transmitted to the rescue team. High-quality video, audio, and image formats offer comprehensive situational awareness, support decision-making, and improve overall operational efficiency.

Design of a traceability system for a coffee supply chain based on blockchain and machine learning

Purpose: This paper aims to develop a coffee supply chain traceability system based on Blockchain (BC) and Machine Learning (ML) with the aim of ensuring the quality of coffee beans production. BC functions to ensure supply chain performance, while the ML model ensures product quality.Design/methodology/approach: Smart Contracts will be built on the Ethereum Virtual Machine BC network based on Ethereum. The ML model to identify good and bad green coffee beans will be built using different YOLO algorithms, which will go through training and validation stages, namely using the k-fold cross validation method. The ML model algorithm is based on Convolutional Neural Network (CNN) using YOLOv5m, YOLOv6m and YOLOv7. The best model will be chosen based on the results of cross-validation with test data in the form of coffee image data that the model has never seen (unseen data). The whole process of building the ML model is done on the Google Collab Pro+ Virtual Machine.Findings: YOLOv5m outperformed the other models in both non-augmented and augmented training datasets, highlighting the proficiency of YOLOv5m in managing compact datasets and its resilience in the face of data augmentation, positioning it as a prime selection for quality discernment tasks within the realm of green coffee beans. The smart contracts offer an all-encompassing approach for user management, monitoring product status, and presenting traceability data within the framework of coffee plantation administration.Originality/value: This research contributes to the development of blockchain network as a solution to implement traceability systems along coffee supply chains in Indonesia. Moreover, it shows that while blockchain can ensure the process of production along the coffee supply to follow certain guidelines, machine learning can verify whether the product that was produced by utilizing BC is of high quality/acceptable.

Real-time sign language recognition based on YOLO algorithm

This study focuses on real-time hand gesture recognition in the Turkish sign language detection system. YOLOv4-CSP based on convolutional neural network (CNN), a state-of-the-art object detection algorithm, is used to provide real-time and high-performance detection. The YOLOv4-CSP algorithm is created by adding CSPNet to the neck of the original YOLOv4 to improve network performance. A new object detection model has been proposed by optimizing the YOLOv4-CSP algorithm in order to provide more efficient detection in Turkish sign language. The model uses CSPNet throughout the network to increase the learning ability of the network. However, Proposed YOLOv4-CSP has a learning model with Mish activation function, complete intersection of union (CIoU) loss function and transformer block added. The Proposed YOLOv4-CSP algorithm has faster learning with transfer learning than previous versions. This allows the proposed YOLOv4-CSP algorithm to perform a faster restriction and recognition of static hand signals simultaneously. To evaluate the speed and detection performance of the proposed YOLOv4-CSP model, it is compared with previous YOLO series, which offers real-time detection, as well. YOLOv3, YOLOv3-SPP, YOLOv4-CSP and proposed YOLOv4-CSP models are trained with a labeled dataset consisting of numbers in Turkish Sign language, and their performances on the hand signals recognitions are compared. With the proposed method, 98.95% precision, 98.15% recall, 98.55 F1 score and 99.49% mAP results are obtained in 9.8 ms. The proposed method for detecting numbers in Turkish sign language outperforms other algorithms with both real-time performance and accurate hand sign prediction, regardless of background.

Implementing distributed collaboration and applying the YOLO algorithm to robots

Recently, the world has witnessed the development of artificial intelligence and robot programming, which have enabled robots to work together to perform specific assigned tasks while overcoming obstacles in the environment. Robots can now operate independently of each other. In this research, four-wheeled robots were created in webots and placed in different environments in Gazebo. These robots are associated with LIDAR and Kinect cameras. Due to the distributed collaboration between robots, a robot cannot traverse a path that it has previously traversed. The three robots are given the same goal, and the first robot to reach the goal signals the end of the mission to the remaining robots and learns about the surrounding objects on the way to the goal. Therefore, the YOLO algorithm was used (You Only Look Once). This is one of the best algorithms for detecting objects in their environment. Regarding the results obtained in the simulation, the robot performed all the assigned tasks.