Real-time Object Detection Research Articles

Hyperparameter optimization of YOLOv4 tiny for palm oil fresh fruit bunches maturity detection using genetics algorithms

Indonesia's economic progress is closely linked to palm oil, a crucial plantation commodity. The price of this oil relies on the oil content found in the fruit of the oil palm, which is gauged by the fruit's maturity level, a task usually undertaken by experts. The maturity of oil palm FFB is generally divided into several classes, commonly unripe, under-ripe, ripe, and over-ripe. However, the detection of maturity still uses human vision, which causes detection errors. Therefore, this research will use the computer vision method with the YOLOv4 model to detect the maturity of the oil palm fruit. YOLOv4 itself is a real-time object detection model that has been widely used to detect fruit maturity, including oil palm fruit; this research uses 4,160 images of oil palm fruit maturity which is divided into six categories, namely empty fruit, underripe, abnormal, ripe, under-ripe, and over-ripe. The YOLOv4 model trained using the dataset will be optimized using GA for looking at the optimal hyperparameter to produce a better model. The result is YOLOv4 model experienced an increase in mAP compared to before using GA hyperparameter optimization.

Real-Time Object Detection Overview: Advancements, Challenges, and Applications

Real-time object detection is a crucial aspect of computer vision with applications spanning autonomous vehicles, surveillance, robotics, and augmented reality. This study examines real-time object detection techniques, highlighting their significance in artificial intelligence. The primary goal is swift and accurate object identification in images or video streams. Traditional methods like sliding windows and region-based approaches had limitations in computational efficiency. Deep learning, particularly Convolutional Neural Networks (CNNs), revolutionized object detection. Models like SSD, YOLO, and Faster R-CNN excel in accuracy and speed. They employ anchor boxes, feature pyramid networks, and non-maximum suppression to balance precision and processing speed. Hardware accelerators like GPUs, TPUs, and FPGAs facilitate real-time inference.
 Challenges in real-time object detection include occlusion, scale variations, and cluttered environments. Researchers must navigate the trade-offs between accuracy and speed. Real-time object detection is pivotal in computer vision, enabling intelligent systems across diverse applications. The continuous evolution of deep learning algorithms and hardware capabilities pushes the boundaries of this field, making it a dynamic research domain in artificial intelligence.

Improved object detection via large kernel attention

Accurate and real-time object detection is crucial for autonomous vehicles. For in-vehicle computing platforms, a giant model is difficult to achieve in real time and also increases the hardware cost. Moreover, a lightweight model built from a lot of depth-wise separable convolution layers cannot achieve the accuracy required for autonomous driving. We introduce Large Kernel Attention (LKA) technology to decouple the large kernel convolutions. It can combine high accuracy with small computational cost. Furthermore, we use LKA as the basis for designing a new module (Res-VAN) that can be used to build backbone networks. This study focuses on how the Res-VAN module can be deployed to improve the YOLOv5 in order to reduce the computational effort of the model, but maintain its accuracy. We named the model “LKA-YOLO” and validated it on PASCAL VOC dataset, MS COCO 2017 dataset and VirDrone2019 datasets. Experimental results show that LKA-YOLO reduces the computation of YOLOv5 by 57.5% (16 GFLOPs vs. 6.8 GFLOPs). And the feature extraction ability of LKA-YOLO is stronger than YOLOv5 on small datasets. The results on VirDrone2019 dataset show that our proposed method has a great advantage in dealing with tiny objects. Meanwhile, comparing with YOLO family on the COCO 2017 dataset, the metrics of LKA-YOLO are also outstanding.

Traceability System of Sand Core in Casting Production with a Digital-Twin Core Rack

In recent development of casting traceability, it has been noticed that many casting manufacturing processes that involve the use of sand cores often lack proper traceability back to the cores used in their production and the supporting production information. Most marking symbols applied to sand cores using existing marking methods not only exhibit unstable code readability but also pose critical risks to the surface quality of sand cores. In response, we developed and tested a digital-twin core rack aimed at transferring digital codes to sand cores without contact with their surfaces. By utilizing infrared sensors for real-time object detection, the core rack can transfer digital identical codes from the core-making machine to the core rack, and subsequently to the casting machine. This digital code encompasses all relevant process data of a sand core, from core making to de-coring. Moreover, the various sensors embedded in the core rack were designed to monitor air humidity, temperature, and vibration events during the transport and storage of the cores. These parameters are especially critical for maintaining the quality of inorganic sand cores. The study demonstrates the successful application of each component within this conceptual framework. Looking ahead, it is imperative to update the hardware of this concept to ensure its adaptation to the demanding conditions of an industrial environment.

High-performance Reconfigurable DNN Accelerator on a Bandwidth-limited Embedded System

Deep convolutional neural networks (DNNs) have been widely used in many applications, particularly in machine vision. It is challenging to accelerate DNNs on embedded systems because real-world machine vision applications should reserve a lot of external memory bandwidth for other tasks, such as video capture and display, while leaving little bandwidth for accelerating DNNs. In order to solve this issue, in this study, we propose a high-throughput accelerator, called reconfigurable tiny neural network accelerator (ReTiNNA), for the bandwidth-limited system and present a real-time object detection system for the high-resolution video image. We first present a dedicated computation engine that takes different data mapping methods for various filter types to improve data reuse and reduce hardware resources. We then propose an adaptive layer-wise tiling strategy that tiles the feature maps into strips to reduce the control complexity of data transmission dramatically and to improve the efficiency of data transmission. Finally, a design space exploration (DSE) approach is presented to explore design space more accurately in the case of insufficient bandwidth to improve the performance of the low-bandwidth accelerator. With a low bandwidth of 2.23 GB/s and a low hardware consumption of 90.261K LUTs and 448 DSPs, ReTiNNA can still achieve a high performance of 155.86 GOPS on VGG16 and 68.20 GOPS on ResNet50, which is better than other state-of-the-art designs implemented on FPGA devices. Furthermore, the real-time object detection system can achieve a high object detection speed of 19 fps for high-resolution video.

Lite-YOLOv3: a real-time object detector based on multi-scale slice depthwise convolution and lightweight attention mechanism

Lite-YOLOv3: a real-time object detector based on multi-scale slice depthwise convolution and lightweight attention mechanism

Ensemble-Based Machine Learning Approach for Real-Time Person Counting in an Instant Attendance System

Real-time attendance systems have become indispensable in various domains, including educational institutions and workplaces, as they automate attendance tracking and improve efficiency. This paper introduces a robust real-time attendance system that combines OpenCV and the You Only Look Once (YOLO) model. By integrating computer vision and deep learning techniques, the system achieves accurate and rapid face detection and recognition. Our proposed system utilizes OpenCV, a powerful computer vision library, to capture video streams from cameras. The YOLO model, a cutting-edge real-time object detection algorithm, is employed to identify and localize faces within the video frames. Thanks to YOLO's efficiency, the system ensures real-time processing, enabling seamless attendance recording. To enhance accuracy, the system employs a two-step approach consisting of face detection and face recognition. During the face detection phase, the YOLO model detects bounding boxes around faces. Subsequently, the system matches these detected faces against a pre-existing database of enrolled individuals using face recognition techniques. To improve performance, transfer learning techniques are applied to fine-tune the YOLO model on a diverse dataset containingvarious face images. This adaptation process ensures high precision and recall rates, even in challenging conditions such as varying lighting and occlusion. Experimental results demonstrate the effectiveness of the proposed real-time attendance system, achieving a high accuracy rate suitable for practical applications. Its real-time performance allows for seamless integration into existing attendance management workflows, resulting in time savings and improved administrative processes. The core intention of this paperwork is to develop a GUI page using Python programming, which will display the number of students who are present and number of students who are absent along with the number of students when section details are entered manually. In addition to this, pictures of the students who are present are displayed on the framework. It is called the real-time face detection which is very beneficial for managingacademic activity.

REAL-TIME OBJECT DETECTION IN VIDEOS USING DEEP LEARNING MODELS

Video object detection plays a pivotal role in various applications, from surveillance to autonomous vehicles. This research addresses the need for real-time object detection in videos using advanced deep learning models. The current landscape of object detection techniques often struggles to maintain efficiency in processing video streams, leading to delays and resource-intensive computations. This study aims to bridge this gap by proposing a novel methodology for real-time object detection in videos. With the surge in video data across domains, the demand for swift and accurate object detection in real-time has become imperative. Existing methods face challenges in balancing speed and precision, prompting the exploration of more robust solutions. This research endeavors to enhance the efficiency of video object detection, offering a timely and accurate approach to address contemporary demands. The primary challenge lies in achieving real-time object detection without compromising accuracy. Traditional methods often compromise speed for precision, leading to inadequate performance in dynamic video environments. This study seeks to overcome this dilemma by introducing a methodology that optimizes both speed and accuracy, catering to the real-time constraints of video processing. Despite the advancements in object detection, a notable research gap exists in the domain of real-time video object detection. Existing models exhibit limitations in adapting to the dynamic nature of video streams, necessitating the development of novel methodologies. This research aims to fill this void by proposing an innovative approach that addresses the specific challenges posed by real-time video data. The proposed methodology integrates state-of-the-art deep learning models, optimizing them for real-time video object detection. Leveraging advanced architectures and streamlining the inference process, the model aims to provide accurate detections at unparalleled speeds. Additionally, a novel data augmentation technique is introduced to enhance the model’s adaptability to dynamic video scenarios. Preliminary results demonstrate the effectiveness of the proposed methodology, showcasing a significant improvement in both real-time processing speed and object detection accuracy. The model exhibits promising performance across diverse video datasets, highlighting its potential to outperform existing methods in real-world applications.

ELWNet: An Extremely Lightweight Approach for Real-Time Salient Object Detection

Existing lightweight salient object detection (SOD) methods aim to solve the problem of high computational costs that is prevalent with heavyweight methods. However, compared with heavyweight methods, the detection accuracy of lightweight methods is greatly reduced while real-time performance is not significantly improved. Therefore, we aim to establish a trade off between computational cost and detection performance by improving the network efficiency. We propose a fast and extremely lightweight end-to-end wavelet neural network (ELWNet) for real-time salient object detection. ELWNet can achieve salient object detection and segmentation at approximately 70FPS (GPU), 19FPS (CPU) with 76K parameters and 0.38G FLOPs. We introduce wavelet transform theory into a neural network, proposing a wavelet transform module (WTM), a wavelet transform fusion module (WTFM), a novel feature residual mechanism, and construct an efficient architecture. The wavelet transform theory is integrated into the neural network to realize the interaction between the features in the frequency and the time domain. Meanwhile, ELWNet does not rely on a pre-trained model, which significantly reduces redundant features. We validate the performance of ELWNet using five well-known datasets, and demonstrate state-of-the-art performance compared with 24 other SOD models in terms of being lightweight, detection accuracy and real-time capabilities. Our method maintains high detection performance while reducing the number of model parameters by approximately 99% compared with heavyweight methods.

Fine-tuned YOLOv5 for real-time vehicle detection in UAV imagery: Architectural improvements and performance boost

Nowadays, Unmanned Aerial Vehicles (UAVs) have become useful for various civil applications, such as traffic monitoring and smart parkings, where real-time vehicle detection and classification is one of the key tasks. There are many challenges in detecting vehicles including small size objects and the variety in the UAV’s altitude and angle. As classic object detection solutions have limitations in confronting these challenges, recent methods are developed based on convolutional neural networks and their ability in effective feature learning. Due to the computational complexity in these networks and the need for accurate and real-time object detection, balancing the accuracy and inference speed is obligatory for efficiency. This paper aims to propose an accurate, efficient and real-time vehicle detection network based on the successful YOLOv5 object detection model. This is done by improving the structure of the model, adding attention mechanism and using an adaptive bounding box regression loss function. Also, considering the need for real-time inference speed, the depth and width of the model was balanced and ghost convolution was incorporated into the Neck unit to further improve the balance between accuracy and inference speed. The proposed method is evaluated on three different urban UAV imagery datasets, VisDrone, CARPK and VAID, specifically intended for civil applications. Comparing the obtained results from the proposed method with YOLOv5 baseline models, it achieved 3.52% higher mAP50 and 207.15% higher FPS than YOLOv5X on VisDrone dataset, while it is much smaller in size and GFLOPS. Totally, the proposed network outcomes show how the applied structural and conceptual modifications can upgrade the YOLO family towards being small in size, high in accuracy and fast in inference speed.

Towards Real-Time Multi-Class Object Detection and Tracking for the FLS Pattern Cutting Task

Towards Real-Time Multi-Class Object Detection and Tracking for the FLS Pattern Cutting Task

Autonomous Drone Navigation

Autonomous drone navigation has evolved significantly as a result of the integration of cutting-edge computer vision and reinforcement learning algorithms. In this study, we propose a comprehensive framework that combines the adaptability of the TD3 neural network, the agility of the Ros2 platform, and the resilience of the YOLOv3 object detection model. Making use of YOLOv3's real-time object detection capabilities, our system proves to be highly adept at recognizing and reacting to changing environmental impediments, which guarantees improved UAV situational awareness. When combined with the TD3 neural network, the Ros2-based navigation framework allows for quick decision- making, which allows the drone to move independently toward predetermined destination points while effectively avoiding obstructions. Our work demonstrates the effectiveness of our suggested strategy through thorough experimentation in simulated situations, highlighting its potential for practical implementation in a variety of applications, from autonomous delivery services to aerial surveillance. The outcomes demonstrate the integrated system's flexibility and resilience, highlighting its capacity to successfully handle challenging navigational situations and guarantee secure and effective aerial operations. Our study adds to the field of autonomous drone navigation by highlighting the vital role that integrated computer vision and machine learning techniques play in enabling smart and flexible unmanned aerial vehicle operations. Keywords— Autonomous drone, YOLOv3, Object detection, Ros2, TD3, Reinforcement learning, Simulated environment, Aerial navigation, obstacle avoidance, Computer vision, Unmanned aerial vehicles (UAVs),Goal-point navigation, Aerial surveillance

A Novel Swarm Unmanned Aerial Vehicle System: Incorporating Autonomous Flight, Real-Time Object Detection, and Coordinated Intelligence for Enhanced Performance

A Novel Swarm Unmanned Aerial Vehicle System: Incorporating Autonomous Flight, Real-Time Object Detection, and Coordinated Intelligence for Enhanced Performance

Enhancement of Real-Time Object Detection and Tracking in Collaborative Environment using AI and Mixed Reality

The area of mixed reality has had rapid growth in recent years, with a notable rise in funding. This may be attributed to the rising recognition of the potential advantages associated with the integration of virtual information into the physical environment. The majority of contemporary mixed reality apps that rely on markers use algorithms for local feature identification and tracking. This study aims to enhance the accuracy of object recognition in complicated environment and enable real-time classification operations via the introduction of a unique detection approach known as the lightweight and efficient YOLOv4 model. In the present setting, Computational vision emerges as a very valuable and engaging manifestation of artificial intelligence (AI) that finds widespread application in many aspects of daily existence. The field of computer vision is dedicated to the development of advanced artificial intelligence and computer systems that aim to replace complex elements of the human environment. In recent times, deep neural networks have emerged as a crucial component in several sectors owing to their well-established capacity to process visual input. This study presents a methodology for classifying and identifying objects using the YOLOv4 object detection algorithm. Convolutional neural networks (CNNs) have shown exceptional efficacy in the tasks of object tracking and feature extraction from pictures. Therefore, the enhanced network architecture optimizes both the precision of identification and the speed at which it operates. This research will contribute to developing mixed-reality simulations system for object detection and tracking in collaborative environment that are accessible to everyone, including users in the architectural filed. The model was evaluated in comparison to other object detection approaches. Based on the empirical results, it was observed that the YOLOv4 model exhibited a mean average precision (mAP) of 0.988, surpassing the performance of both YOLOv3 and other object identification models.

Target tracking and detection based on YOLOv5 algorithm

The YOLOv5 algorithm has gained popularity in recent years as an effective solution for real-time object detection in images and videos. This paper explores its potential for solving the problem of target tracking detection by proposing a modified YOLOv5 architecture that integrates object detection and tracking capabilities.The proposed YOLOv5-based tracking system includes three major components: object detection, object tracking, and object association. The object detection component uses a YOLOv5 model to detect and localize the target object in each frame of the video. The object tracking component then tracks the target object across frames using a Kalman filter and a Hungarian algorithm for data association. Finally, the object association component uses a motion model to handle occlusions and re-identifies the target object when it reappears in the field of view.The performance of the proposed YOLOv5-based tracking system is evaluated on several benchmark datasets, and its results are compared to state-of-the-art tracking algorithms. The experimental results show that the system achieves competitive tracking accuracy and real-time processing speed. Additionally, the effectiveness of the proposed motion model for handling occlusions and re-identification of the target object is demonstrated. In conclusion, the YOLOv5 algorithm has promising potential for target tracking detection in real-world scenarios, and it could have various applications in surveillance, robotics, and autonomous driving.

Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review

Real-time object detection based on UAV remote sensing is widely required in different scenarios. In the past 20 years, with the development of unmanned aerial vehicles (UAV), remote sensing technology, deep learning technology, and edge computing technology, research on UAV real-time object detection in different fields has become increasingly important. However, since real-time UAV object detection is a comprehensive task involving hardware, algorithms, and other components, the complete implementation of real-time object detection is often overlooked. Although there is a large amount of literature on real-time object detection based on UAV remote sensing, little attention has been given to its workflow. This paper aims to systematically review previous studies about UAV real-time object detection from application scenarios, hardware selection, real-time detection paradigms, detection algorithms and their optimization technologies, and evaluation metrics. Through visual and narrative analyses, the conclusions cover all proposed research questions. Real-time object detection is more in demand in scenarios such as emergency rescue and precision agriculture. Multi-rotor UAVs and RGB images are of more interest in applications, and real-time detection mainly uses edge computing with documented processing strategies. GPU-based edge computing platforms are widely used, and deep learning algorithms is preferred for real-time detection. Meanwhile, optimization algorithms need to be focused on resource-limited computing platform deployment, such as lightweight convolutional layers, etc. In addition to accuracy, speed, latency, and energy are equally important evaluation metrics. Finally, this paper thoroughly discusses the challenges of sensor-, edge computing-, and algorithm-related lightweight technologies in real-time object detection. It also discusses the prospective impact of future developments in autonomous UAVs and communications on UAV real-time target detection.

Dynablox: Real-Time Detection of Diverse Dynamic Objects in Complex Environments

Dynablox: Real-Time Detection of Diverse Dynamic Objects in Complex Environments

RTAD: A Real-Time Animal Object Detection Model Based on a Large Selective Kernel and Channel Pruning

Animal resources are significant to human survival and development and the ecosystem balance. Automated multi-animal object detection is critical in animal research and conservation and ecosystem monitoring. The objective is to design a model that mitigates the challenges posed by the large number of parameters and computations in existing animal object detection methods. We developed a backbone network with enhanced representative capabilities to pursue this goal. This network combines the foundational structure of the Transformer model with the Large Selective Kernel (LSK) module, known for its wide receptive field. To further reduce the number of parameters and computations, we incorporated a channel pruning technique based on Fisher information to eliminate channels of lower importance. With the help of the advantages of the above designs, a real-time animal object detection model based on a Large Selective Kernel and channel pruning (RTAD) was built. The model was evaluated using a public animal dataset, AP-10K, which included 50 annotated categories. The results demonstrated that our model has almost half the parameters of YOLOv8-s yet surpasses it by 6.2 AP. Our model provides a new solution for real-time animal object detection.

Effects of Data Augmentation Methods on YOLO v5s: Application of Deep Learning with Pytorch for Individual Cattle Identification

In this paper, we investigate the performance of the YOLO v5s (You Only Look Once) model for the identification of individual cattle in a cattle herd. The model is a popular method for real-time object detection, accuracy, and speed. However, since the videos obtained from the cattle herd consist of free space images, the number of frames in the data is unbalanced. This negatively affects the performance of the YOLOv5 model. First, we investigate the model performance on the unbalanced initial dataset obtained from raw images, then we stabilize the initial dataset using some data augmentation methods and obtain the model performance. Finally, we built the target detection model and achieved excellent model performance with an mAP (mean average precision) of 99.5% on the balanced dataset compared to the model on the unbalanced data (mAP of 95.8%). The experimental results show that YOLO v5s has a good potential for automatic cattle identification, but with the use of data augmentation methods, superior performance can be obtained from the model.

CAR VEHICLE IMAGE OBJECT DETECTION USING YOU ONLY LIVE ONCE (YOLO)

This research aims to analyze the performance of image object detection methods using You Only Live Once (YOLO) specifically in the context of car detection. YOLO-based object detection methods have gained great attention in the artificial intelligence community due to their ability to perform real-time object detection. In this research, we focus on using YOLO to detect car objects in images. The YOLO method will be tested for performance using a dataset of car images that have been collected from various sources. This dataset includes various lighting conditions, backgrounds, and car positions. The training process will be performed using the YOLO architecture that has been pre-trained with an extensive dataset. In the testing phase, the performance of the YOLO method in car object detection will be evaluated using standard evaluation metrics such as precision, detection speed, and recall. The results of this study will show the success rate of YOLO in car detection in images and provide a better understanding of the limitations and advantages of this method. The conclusion of this research is expected to provide valuable insight into the use of the YOLO method in car object detection. This information can be used as a basis for the development and improvement of YOLO-based object detection methods, and can be applied in various applications such as automated vehicle security systems and traffic analysis.