Real-time Object Detection System Research Articles

A deep learning framework for automated anomaly detection and localization in fused filament fabrication

Under-extrusion poses a common challenge in filament 3D printing, necessitating precise adjustments to printing parameters for optimal resolution. Swift identification of this flaw is crucial for implementing timely corrective measures. In response, we present a novel machine learning approach designed to detect anomalies in 3D printing, specifically in the fused filament fabrication process (FFF). Our framework utilizes “You Only Look Once” (YOLO), a real-time object detection system, and “Visual Geometry Group-16” (VGG-16), a Convolutional Neural Network (CNN) model for image recognition, to accurately identify and localize under-extrusion events. Initially, models such as VGG-16, VGG-19, and ResNet-50 were trained without the inclusion of YOLO to establish baseline accuracies. Subsequently, an automated image pre-processing phase employs YOLO to discern the nozzle head, facilitating subsequent cropping around the region of interest for retraining the models. This inclusion of the nozzle head detection approach significantly improved the accuracy of all models, with the combined application of YOLO and VGG-16 demonstrating the most substantial enhancement, boosting detection accuracy to 97%. Moreover, Gradient-weighted Class Activation Mapping (Grad-CAM) technique, another CNN-based method, is employed to effectively highlight predicted areas in under-extrusion scenarios. While our method significantly advances the automatic detection of printing anomalies, it primarily serves as a diagnostic tool, signaling the need for intervention. Our rigorous testing on images of varying complexities confirms the model’s robustness, evaluated using comprehensive metrics such as precision, recall, and F1 score.

Real-time object detection and augmentation

This study presents the development of a real-time object detection and augmentation system using TensorFlow and Unity it leverages TensorFlow model to identify and classify objects in real-time from a camera feed, and Unity to integrate and display corresponding 3D virtual objects in an augmented reality environment. By creating a mapping system to pair detected objects with their virtual counterparts, the system aims to enhance user interaction through dynamic and immersive AR experiences. The study addresses the challenges of real-time processing and seamless integration, demonstrating the potential of combining machine learning and augmented reality to enrich interactive applications across various domains.

Precise soil coverage in potato planting through plastic film using real-time image recognition with YOLOv4-tiny

Planting potatoes through plastic film with incomplete or excessive soil coverage over seed holes significantly impairs yield. Existing covering methods rely solely on mechanical transmissions, leading to bulky and inconsistent soil coverage of the seed holes. This paper reports an innovative method using a precise soil covering device based on the YOLOv4-tiny real-time object detection system to accurately identify potato plastic film holes and cover them with soil. The system adopts a lightweight and high-precision detection scheme, balancing increased network depth with reduced computation. It can identify holes in the plastic film in real-time and with high accuracy. To verify the effectiveness of YOLOv4-tiny real-time object detection system, a precise soil covering device based on this detection system has been designed and applied to a double crank multi-rod hill-drop planter. Field tests revealed that the system's average accuracy rate for detecting holes is approximately 98%, with an average processing time of 15.15 ms per frame. This fast and accurate performance, combined with the device's robust real-time operation and anti-interference capabilities during soil covering, effectively reduce the problems of soil cover omission and repeated covering caused by existing mechanical transmission methods. The findings reported in this paper are valuable for the development of autonomous potato plastic film precise soil covering devices for commercial use.

Multi-Object Detection and Tracking with Modified Optimization Classification in Video Sequences

The paper presents a novel approach to enhancing multi-object detection and tracking in video sequences using a Modified Ant Swarm Optimization Deep Learning (ASO-DL) algorithm. The ASO-DL algorithm synergistically combines the optimization capabilities of ant swarm optimization with the powerful feature extraction abilities of deep learning models, resulting in a robust framework for realtime video analytics. Extensive simulations and experiments demonstrate significant improvements in key performance metrics, including accuracy, precision, recall, and F1 score, across various iterations. The proposed method consistently outperforms baseline models, achieving a final best fitness value of 0.96, with an accuracy of 0.98, precision of 0.99, and recall of 0.95. Additionally, classification results across different datasets such as CIFAR-10, IMDB, COCO, and ImageNet highlight the algorithm’s versatility and effectiveness. This research contributes to the field by providing a highly optimized solution for complex multi-object tracking tasks, offering substantial advancements in the accuracy and efficiency of real-time object detection systems. The findings hold significant potential for applications in surveillance, autonomous vehicles, and other domains requiring precise and reliable multi-object tracking.

Human Detection and Counting using YOLOV4

Object detection is a fundamental problem in computer vision that involves detecting and localizing objects within an image. In recent years, convolutional neural networks (CNNs) have become the state-of-the-art approach for object detection tasks. YOLO (You Only Look Once) is a popular real-time object detection system that uses a single CNN to predict the bounding boxes and class probabilities for objects in an image. In this paper, we present a human detection and counting system using YOLO. Our system is trained on a large dataset of annotated images and achieves high accuracy and real-time performance. We evaluate our system on a public dataset and demonstrate its effectiveness in various scenarios. This paper focuses on developing a system for human detection and counting using YOLOv4 and Flask API. The system aims to address the increasing importance of human detection and counting in various fields such as social distancing, crowd management, and surveillance.

IoT-based real-time object detection system for crop protection and agriculture field security

IoT-based real-time object detection system for crop protection and agriculture field security

Real-Time Object Detection and Recognition with Computer Vision

Abstract: Object detection, a fundamental aspect of computer vision, is essential for identifying and localizing objects within images or video frames, leveraging advancements in deep learning, particularly convolutional neural networks (CNNs), to enhance precision and speed. Its applications span diverse domains, from autonomous vehicles and surveillance systems to augmented reality and human-computer interaction. Our project focuses on engineering a real-time object detection system, integrating deep learning and computer vision methodologies. Anchored on the robust Single Shot Multibox Detector (SSD) architecture and reinforced by the efficiency and accuracy of the MobileNetV3 backbone, our system utilizes a pre-trained SSD MobileNetV3 model and comprehensive annotations from the COCO dataset to adeptly detect and recognize a wide array of objects within live video streams or archived footage. It seamlessly processes video frames from various sources, annotating detected objects in real-time to provide instant visual feedback. Offering customizable confidence thresholds and support for multiple video sources, our project showcases the transformative potential of deep learning and computer vision, advancing realtime object detection across domains like surveillance and interactive systems. By pushing the boundaries of object detection technology, our project aims to enhance safety, efficiency, and user experiences in various applications, promising to redefine the landscape of computer vision with innovation and advancement.

Advanced method for Image detection using OpenCV

Abstract: In computer vision, object detection is an essential job with many real-world applications, such as robots, autonomous cars, and surveillance. Because of its great accuracy and speed, the YOLO (You Only Look Once) method is a well-liked realtime object recognition technique that has attracted a lot of attention. This technique is perfect for time-sensitive applications since it examines the full image at once and predicts bounding boxes and class probabilities for recognized items. YOLO has undergone many iterations, with YOLO v5 being the most recent and sophisticated version. It utilizes anchor boxes and a feature pyramid network (FPN) to increase the accuracy of object recognition. Our goal in this research is to apply YOLO v5 to realtime picture and object identification applications stage. The model will be trained using an appropriate dataset, and its performance will be assessed against a range of benchmarks and advanced object detection methods. The project's output will offer a reliable and effective real-time object detection system that will facilitate prompt decision-making in the identification of item types and their corresponding placements. It has useful uses in robotics, autonomous driving, and surveillance.

Wireless Remote Controlled Multi-Purpose Robot using Raspberry PI

Over the past five decades, robotics and associated technologies have evolved into indispensable components across numerous industries, particularly in advanced manufacturing. Moreover, their application has extended beyond production-centric domains to encompass pivotal roles in military and law enforcement sectors, owing to advancements in sophistication, reliability, and compactness. This paper proposes the implementation of a real-time object detection system capable of precisely identifying and locating recognized objects, presenting a significant and practical advancement. Integrating various modules such as GSM and GPS with a Raspberry Pi, along with an array of sensors including metal and flame sensors, an L293 Motor driver, HC-SR04 Ultrasonic sensor, and HC-05 Bluetooth, this system facilitates seamless recognition and localization of objects in real-time. The Bluetooth connectivity feature enables remote control of the device, while a built-in camera captures images of unidentified individuals, thereby enhancing its surveillance capabilities. The incorporation of a motor driver ensures secure and controlled movement of the robot, while the ultrasonic sensor detects obstacles within its proximity. Additionally, the metal sensor swiftly identifies metallic objects, relaying their locations to the administrator, whereas the flame sensor promptly detects fires, enabling the system to dispatch location data for immediate response. This holistic approach renders the proposed system a versatile tool applicable across a spectrum of scenarios, ranging from security and surveillance operations to emergency response initiatives.

Development of Real-Time Unmanned Aerial Vehicle Urban Object Detection System with Federated Learning

In this paper, an urban object detection system via unmanned aerial vehicles (UAVs) is developed to collect real-time traffic information, which can be further utilized in many applications such as traffic monitoring and urban traffic management. The system includes an object detection algorithm, deep learning model training, and deployment on a real UAV. For the object detection algorithm, the Mobilenet-SSD model is applied owing to its lightweight and efficiency, which make it suitable for real-time applications on an onboard microprocessor. For model training, federated learning (FL) is used to protect privacy and increase efficiency with parallel computing. Last, the FL-trained object detection model is deployed on a real UAV for real-time performance testing. The experimental results show that the object detection algorithm can reach a speed of 18 frames per second with good detection performance, which shows the real-time computation ability of a resource-limited edge device and also validates the effectiveness of the developed system.

The detection and classification of acute myeloid leukaemia blood cell images based on different YOLO approaches

Medical image examination with a deep learning approach is greatly beneficial in the healthcare industry for faster diagnosis and disease monitoring. One of the popular deep learning algorithms such as you only look once (YOLO) developed for object detection is a successful state-ofthe-art algorithm in real-time object detection systems. Although YOLO is continuously improving in the object detection area, there are still questions about how different YOLO versions compare in terms of performance. We utilize eight YOLO versions to classify acute myeloid leukaemia (AML) blood cells in image examinations. We also acquired the publicly available AML dataset from the cancer imaging archive (TCIA) which consists of expert-labeled single cell images. Data augmentation techniques are additionally applied to enhance and balance the training images in the dataset. The overall results indicated that eight types of YOLO approaches have outstanding performances of more than 90% in precision and sensitivity. In comparison, YOLOv4-tiny has a more reliable performance than the other seven approaches. Consistently, the YOLOv4-tiny also achieved the highest AUC score. Therefore, this work can potentially provide a beneficial digital rapid tool in the screening and evaluation of numerous haematological disorders.

Object Detection using an Advanced Deep Learning Algorithm: YOLOv4

This research proposes implementing YOLOv4, a real-time object detection system that can accurately and efficiently recognize objects in photos and videos. The goals include creating the YOLOv4 architecture, generating datasets, training the model, evaluating its performance, deploying it in real-world applications, and providing extensive documentation. Deep learning frameworks such as TensorFlow or PyTorch are used in the implementation, along with advanced techniques such as transfer learning and data augmentation. By curating annotated datasets and refining training techniques, the model hopes to attain high accuracy, precision, and recall in object detection tasks. Performance analysis will compare the model's outcomes to those of cutting-edge systems, assessing its strengths and weaknesses. The deployment phase involves integrating the model into existing systems or creating separate applications for real-world scenarios such as pedestrian and vehicle detection. Comprehensive documentation and a user guide will help developers and users make the best use of the trained model. Overall, this research intends to demonstrate YOLOv4's usefulness and feasibility in developing computer vision technology and supporting the creation of intelligent systems with real-time object identification capabilities.

Empowering Independence through Real Time Object Identification and Navigation for People with Disabilities

Recent studies in assistive technologies for visually impaired individuals showcase a diverse range of methodologies, algorithms, and implementations aimed at enhancing their independence. A notable focus revolves around leveraging cutting-edge technologies such as YOLO (You Only Look Once), SSD (Single Shot Multibox Detector), and Faster R-CNN (Region-based Convolutional Neural Network) to develop real-time object detection systems and deep learning-based smartphone navigation solutions . One prevalent theme in these advancements is the incorporation of auditory feedback to facilitate enhanced user interaction. This is achieved through sophisticated text-to-speech conversion and the integration of audio cues. The utilization of auditory cues not only aids in real-time awareness of the surroundings but also significantly contributes to the overall user experience . Despite remarkable progress, challenges persist in the realm of assistive technologies for the visually impaired. Issues such as processing speed, the occurrence of false positives and negatives, and the adaptability of these systems to various environmental conditions remain prominent. These challenges underline the need for continued research and development in this field to address existing limitations and refine the effectiveness of these assistive technologies .In essence, this survey provides a comprehensive understanding of the current landscape of assistive technologies for the visually impaired. By identifying both achievements and existing challenges, it serves as a valuable resource for researchers and practitioners, contributing to ongoing advancements that ensure tailored solutions and improved independence for individuals with visual impairments

ODSen: A Lightweight, Real-Time, and Robust Object Detection System via Complementary Camera and mmWave Radar

ODSen: A Lightweight, Real-Time, and Robust Object Detection System via Complementary Camera and mmWave Radar

Real Time Object Detection Using YOLOv4

Abstract: This research paper introduces a robust real-time object detection system, leveraging the cutting-edge capabilities of the YOLOv4 (You Only Look Once) deep learning framework.The integration of this system with a webcam in the Google Colab environment forms a novel and accessible platform for live object detection. The paper meticulously details the methodology behind theintegration, providing comprehensive insightsinto the intricatesteps taken to achieve seamless real-time detection. Key aspects covered include the configuration and acquisition of YOLOv4 model weights, setup of the Google Colab environment, and the challenges encountered in optimizing and streaming webcam feeds in this unique computational setting. Implementation details are articulated through Python code snippets, offering a practical guide for researchers and practitioners interested in replicating or extending the system. The code encompasses the initialization of YOLOv4, handling real-time webcam feeds, and harnessing GPU acceleration within the Google Colab framework to ensure optimalperformance. Results presented in the paper showcase the efficacy of the integrated system through real-time object detection on webcam feeds. Evaluation metrics, including accuracy and processing speed, provide a quantitative assessment of the system's performance. Comparative analyses against other state-of-the-art object detection methodologies further highlight the strengths and capabilities of the YOLOv4-based system. The discussion section delves into the advantages and limitations of the implemented system, offering insights into potential applications across domains such as surveillance, robotics, and human-computer interaction. The user-friendly nature of the Google Colab environment is emphasized, promoting accessibility to advanced computer visiontechnologies. In conclusion, this research significantly contributes to the democratization of computer vision technologies, providing a comprehensive blueprint for researchers and practitioners to implement real-time object detection systems. The paper concludes with recommendations for future research directions and improvements to further enhance the system's versatility and performance.

A deep learning framework to map riverbed sand mining budgets in large tropical deltas

ABSTRACT Rapid urbanization has dramatically increased the demand for river sand, leading to soaring sand extraction rates that often exceed natural replenishment in many rivers globally. However, our understanding of the geomorphic and social-ecological impacts arising from Sand Mining (SM) remains limited, primarily due to insufficient data on sand extraction rates. Conventionally, bathymetry surveys and compilation of declared amounts have been used to quantify SM budgets, but they are often costly and laborious, or result in inaccurate quantification. Here, for the first time, we developed a Remote Sensing (RS)-based Deep Learning (DL) framework to map SM activities and budgets in the Vietnamese Mekong Delta (VMD), a global SM hotspot. We trained a near real-time object detection system to identify three boat classes in Sentinel-1 imagery: Barge with Crane (BC), Sand Transport Boat (STB), and other boats. Our DL model achieved a 96.1% Mean Average Precision (mAP) across all classes and 98.4% for the BC class, used in creating an SM boat density map at an Intersection over Union (IoU) threshold of 0.50. Applying this model to Sentinel-1, 256,647 boats were detected in the VMD between 2014–2022, of which 17.4% were BC. Subsequently, the annual SM budget was estimated by correlating it with a recent riverbed incision map. Our results showed that, between 2015–2022, about 366 Mm3 of sand has been extracted across the VMD. The annual budget has progressively increased from 34.92 Mm3 in 2015 to 53.25 Mm3 in 2022 (by 52%), with an annual increment of around 2.79 Mm3. At the provincial-scale, Dong Thap, An Giang, Vinh Long, Tien Giang, and Can Tho were the locations of intensive mining, accounting for 89.20% of the total extracted volume in the VMD. Finally, our estimated budgets were validated with previous research that yielded a correlation coefficient of 0.99% (with bias of 2.65%). The automatic DL framework developed in this study to quantify SM budgets has a high potential to be applied to other deltas worldwide also facing intensive SM.

A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS

YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. We present a comprehensive analysis of YOLO’s evolution, examining the innovations and contributions in each iteration from the original YOLO up to YOLOv8, YOLO-NAS, and YOLO with transformers. We start by describing the standard metrics and postprocessing; then, we discuss the major changes in network architecture and training tricks for each model. Finally, we summarize the essential lessons from YOLO’s development and provide a perspective on its future, highlighting potential research directions to enhance real-time object detection systems.

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.

A Real-Time Object Detection Processor With xnor-Based Variable-Precision Computing Unit

Object detection algorithms using convolutional neural networks (CNNs) achieve high detection accuracy, but it is challenging to realize real-time object detection due to their high computational complexity, especially on resource-constrained mobile platforms. In this article, we propose an algorithm-hardware co-optimization approach to designing a real-time object detection system. We first develop a compact object detection model based on a binarized neural network (BNN), which employs a new layer structure, the DenseToRes layer, to mitigate information loss due to deep quantization. We also propose an efficient object detection processor that runs object detection with high throughput using limited hardware rescources. We develop a resource-efficient processing unit supporting variable precision with minimal hardware overheads. Implemented in field-programmable gate array (FPGA), the object detection processor achieves 64.51 frames/s throughput with 64.92 mean average precision (mAP) accuracy. Compared to prior FPGA-based designs for object detection, our design achieves high throughput with competitive accuracy and lower hardware implementation costs.

YOLOv7 Applied to Livestock Image Detection and Segmentation Tasks in Cattle Grazing Behavior, Monitor and Intrusions

You only look once (YOLO) is a state-of-the-art, real-time object detection system. YOLO version 7 (YOLOv7) model is a variant of YOLO. The objective of this paper is to apply YOLOv7 to livestock image detection and segmentation tasks in cattle grazing behavior, monitor and intrusions. Data obtained revealed that YOLOv7 performs better in terms of speed and accuracy with a mAP of 0.95 than the baseline techniques.