Real-time Detection Algorithm Research Articles

Rail Detection Research Status of Multi -Sensing Technology

Traditional rail inspection methods, primarily relying on manual inspection and basic equipment, face significant challenges including low efficiency, poor accuracy, and susceptibility to human error. This paper presents a comprehensive review of the current research status and future trends in multi-sensor rail transit inspection vehicles. The focus is on the integrated application of various sensing technologies, particularly inertial systems and visual sensing, to achieve high-precision and efficient measurement of rail geometrical parameters. An in-depth analysis of key technologies and challenges in railway track inspection is provided, exploring how advanced sensing methods can overcome the limitations of traditional approaches. The paper also examines the market prospects for rail transit inspection, highlighting the growing demand for more sophisticated and reliable inspection systems. Furthermore, potential future research directions in this field are outlined, including the development of AI-powered data analysis, real-time defect detection algorithms, and the integration of emerging technologies such as LiDAR and thermal imaging. This review aims to provide valuable insights for researchers, engineers, and industry professionals working on advancing rail inspection technologies.

Pre-and-post impact fall detection based on support vector machines using inertial and barometric pressure data

Objective: This paper presents a novel real-time algorithm for fall detection, which contextualizes falls by identifying activities occurring both pre- and post-impact utilizing machine learning techniques and wearable sensors. Methodology: The activities selected to contextualize fall events included standing, lying, walking, running, climbing stairs, and using the elevator. Data were collected using an inertial measurement unit and a barometric altimeter positioned on the participants’ lower backs. Thirteen healthy subjects were observed performing the activities and fall events were recorded from five healthy subjects. The proposed algorithm combines thresholding and cascade support vector machines (SVMs), whose robustness is enhanced by a verification process of the subject’s posture aimed at determining the occurrence of the fall more accurately. Results: The performance of the algorithm was evaluated in terms of the hit rate (HT) both offline and in real-time. From the activities studied, stairs climbing proved to be the most challenging to detect, with an offline HT of 85% and an online HT of 76 %. The overall offline performance was superior, with an HT of 96 %, compared to the performance achieved online, an HT of 91 %; in both cases the fall detection HT was 100 %. Conclusions: The algorithm can be used to recognize fall events occurring to any user, as it has the advantage of not needing prior adaptation due to the nonlinear nature of the SVMs. The cascade SVMs allow for using small sets of variables, leading to low computational cost and a suitable real-time implementation. These features, in addition to the posture verification process, make our algorithm suitable for activity recognition in non-laboratory environments.

Improved Real-Time Detection Transformer-Based Rail Fastener Defect Detection Algorithm

To address the issues of the Real-Time DEtection TRansformer (RT-DETR) object detection model, including poor defect feature extraction in the task of rail fastener defect detection, inefficient use of computational resources, and suboptimal channel attention in the self-attention mechanism, the following improvements were made. Firstly, a Super-Resolution Convolutional Module (SRConv) was designed as a separate component and integrated into the Backbone network, which enhances the image details and clarity while preserving the original image structure and semantic content. This integration improves the model’s ability to extract defect features. Secondly, a channel attention mechanism was integrated into the self-attention module of RT-DETR to enhance the focus on feature map channels, addressing the problem of sparse attention maps caused by the lack of channel attention while saving computational resources. Finally, the experimental results show that compared to the original model, the improved RT-DETR-based rail fastener defect detection algorithm, with an additional 0.4 MB of parameters, achieved a higher accuracy, with a 2.8 percentage point increase in the Mean Average Precision (mAP) across IoU thresholds from 0.5 to 0.9 and a 1.7 percentage point increase in the Average Recall (AR) across the same thresholds.

Bud-YOLO: A Real-Time Accurate Detection Method of Cotton Top Buds in Cotton Fields

Cotton topping plays a crucial and indispensable role in controlling excessive growth and enhancing cotton production. This study aims to improve the operational efficiency and accuracy of cotton topping robots through a real-time and accurate cotton top bud detection algorithm tailored for field operation scenarios. We propose a lightweight structure based on YOLOv8n, replacing the C2f module with the Cross-Stage Partial Networks and Partial Convolution (CSPPC) module to minimize redundant computations and memory access. The network’s neck employs an Efficient Reparameterized Generalized-FPN (Efficient RepGFPN) to achieve high-precision detection without substantially increasing computational cost. Additionally, the loss calculation of the optimized prediction frame was addressed with the Inner CIoU loss function, thereby enhancing the precision of the model’s prediction box. Comparison experiments indicate that the Bud-YOLO model is highly effective for detecting cotton top buds, with an AP50 of 99.2%. This performance surpasses that of other YOLO variants, such as YOLOv5s and YOLOv10n, as well as the conventional Faster R-CNN model. Moreover, the Bud-YOLO model exhibits robust performance across various angles, occlusion conditions, and bud morphologies. This study offers technical insights to support the migration and deployment of the model on cotton topping machinery.

THDet: A Lightweight and Efficient Traffic Helmet Object Detector based on YOLOv8

Traffic helmet object detection is playing an increasing important role in the smart traffic fields. However, object size variation and small-shaped helmet detection has still been a challenging problem by reason of their poor visual appearance in the image. In this work, we present an efficient traffic helmet detector through feature enhancement and lightweight design based on YOLOv8n called THDet. Specifically, we employ the coordinate attention into C2f blocks combined with softmax activate function to achieve feature channel aggregation and strong non-linear expression of the backbone for further effective feature extraction; Next, Focal_CIoU loss function embedded with Focal Loss method is utilized for the more precise measure of various objects bounding box regression and balance of positive and negative examples during training; Then, a new lightweight detection head style is designed only with two proper position heads (P3 & P4) to perform final classification and localization, through this scheme saving the 33.7% parameters than baseline method. Finally, Attention Refined Features Module (ARFM) is built to calibrate the multi-scale fused features by introducing 3-D weights generated from SimAttention to boost the final detection accuracy. Extensive experiments have demonstrated that our proposed method realizes noticeable performance in terms of detection accuracy and inference speed compared with baseline YOLOv8n and many end-to-end detectors of similar model size. Concretely, THDet achieves 0.447 at the overall evaluation metric of mAP0.5−0.95, accomplishing 3.2% detection accuracy improvement than YOLOv8n. Besides, THDet only holds 2.2M parameters with 295 FPS inference speed, reducing 33.4% parameters compared with YOLOv8n. The experimental results validate the effectiveness of our proposed method, showcasing that THDet outperforms the mainstream real-time detection algorithms in the terms of accuracy, inference speed and lightweight model design for traffic helmet object detection.

Coal-rock interface real-time recognition based on the improved YOLO detection and bilateral segmentation network

To improve the accuracy and efficiency of coal-rock interface recognition, this study proposes a model built on the real-time detection algorithm, you only look once (YOLO), and the lightweight bilateral segmentation network. Simultaneously, the regional similarity transformation function and dragonfly algorithm are introduced to enhance the quality of coal-rock images. The comparison with three other models demonstrates the superior edge inference performance of the proposed model, achieving a mean Average Precision (mAP) of 90.2 at the Intersection over Union (IoU) threshold of 0.50 (mAP50) and 81.4 across a range of IoU thresholds from 0.50 to 0.95 (mAP[50,95]). Furthermore, to maintain high accuracy and real-time recognition capabilities, the proposed model is optimized using the open visual inference and neural network optimization toolkit, resulting in a 144.97% increase in the mean frames per second. Experimental results on four actual coal faces confirm the efficacy of the proposed model, showing a better balance between accuracy and efficiency in coal-rock image recognition, which supports further advancements in coal mining intelligence.

Distributed acoustic sensing signal event recognition and localization based on improved YOLOv7

Abstract The distributed acoustic sensing (DAS) system based on phase-sensitive optical time domain reflection ( Φ-OTDR) technology is widely used in pipeline safety monitoring, perimeter security, structure monitoring, etc. Accurate localization and recognition of multi-scene events over long distances has always been a challenge. This paper proposes an improved YOLOv7 algorithm for multi-event real-time detection of DAS system. The algorithm employs space-to-depth Conv(SPD-Conv) to replace the strided convolutions and pooling operations in YOLOv7, reducing fine-grained information loss and learning of inefficient feature representations. In addition, the Convolutional Block Attention Module (CBAM) is introduced in YOLOv7 to improve the model performance. We collected spatial–temporal signal data for six types of pipeline safety events, and passed them into the improved YOLOv7 algorithm in the form of data matrixes for training and evaluation. Experiments have shown that the proposed method achieves an mAP@.5 (mean Average Precision) of 99.7% for the identification of six pipeline safety event types. Positioning loss reduced to 0.2%, and detection speed can reach 70 frames per second(FPS). Our scheme achieves significant improvements in localization and classification accuracy compared to Faster R-CNN, etc. The event recognition localization method proposed in this paper has the advantage of fast speed and high accuracy.

Multimodal Convolutional Neural Network-Based Algorithm for Real-Time Detection and Differentiation of Malignant and Inflammatory Biliary Strictures in Cholangioscopy: a Proof of Concept Study with Video

Deep learning algorithms gained attention for detection (CADe) of biliary tract cancer (BTC) in digital single-operator cholangioscopy (dSOC). We developed a multimodal convolutional neural network (CNN) for detection (CADe) characterization and discriminating (CADx) between malignant, inflammatory and normal biliary tissue in raw dSOC videos. In addition, clinical metadata was included in the CNN algorithm to overcome limitations of image-only models. Based on dSOC videos and images of 111 patients (total of 15,158 still frames), we developed and validated a real-time CNN-based algorithm for CADe and CADx. We established an image-only model and metadata injection approach. In addition, we validated frame-wise and case-based predictions on complete dSOC video sequences. Model embeddings were visualized and class-activation maps highlighted relevant image regions. The concatenation-based CADx approach achieved a per-frame AUC of 0.871, sensitivity of 0.809 (95% CI: [0.784-0.832]), specificity of 0.773 [0.761-0.785], PPV of 0.450 [0.423-0.467], and NPV of 0.946 [0.940-0.954] with respect to malignancy on 5,715 test frames from complete videos of 20 patients. For case-based diagnosis using average prediction scores, six out of eight malignant cases and all twelve benign cases were identified correctly. Our algorithm distinguishes malignant and inflammatory bile duct lesions in dSOC videos, indicating the potential of CNN-based diagnostic support systems for both, CADe and CADx. The integration of non-image data can improve CNN based support systems, targeting current challenges in the assessment of biliary strictures.

Lightweight Water Surface Object Detection Network for Unmanned Surface Vehicles

The detection algorithms for water surface objects considerably assist unmanned surface vehicles in rapidly perceiving their surrounding environment, providing essential environmental information and evaluating object attributes. This study proposes a lightweight water surface target detection algorithm called YOLO-WSD (water surface detection), based on YOLOv8n, to address the need for real-time, high-precision, and lightweight target detection algorithms that can adapt to the rapid changes in the surrounding environment during specific tasks. Initially, we designed the C2F-E module, enriched in gradient flow compared to the conventional C2F module, enabling the backbone network to extract richer multi-level features while maintaining lightness. Additionally, this study redesigns the feature fusion network structure by introducing low-level features and achieving multi-level fusion to enhance the network’s capability of integrating multiple levels. Meanwhile, it investigates the impact of channel number differences in the Concat module fusion on model performance, thereby optimizing the neural network structure. Lastly, it introduces the WIOU localization loss function to bolster model robustness. Experiments demonstrated that YOLO-WSD achieves a 4.6% and 3.4% improvement in mAP0.5 on the water surface object detection dataset and Seaship public dataset, respectively, with recall rates improving by 5.4% and 8.5% relative to the baseline YOLOv8n model. The model’s parameter size is 3.3 M. YOLO-WSD exhibits superior performance compared to other mainstream lightweight algorithms.

Implementation of Sibi Sign Language Realtime Detection Program (Case Studi At Sekolah Luar Biasa Negeri 1 Tabanan)

Indonesian deaf people utilize SIBI to communicate using spoken words, gestures, facial expressions, and body language. SIBI, certified for Special Schools (SLB), helps deaf pupils communicate. This project implements SIBI (Indonesian Sign Language System) a real-time detection algorithm at Sekolah Luar Biasa Negeri 1 Tabanan using image processing and YoloV8 ultralytics deep learning. The program trains a sign language gesture detection model on Google Colab's GPU. The SIBI sign language images were used to train a YoloV8 object detection model. The camera captures movements, which the YoloV8 algorithm trained on SIBI gesture data processes. It can recognize gestures in real time and generate text to non-sign language users. The dataset has 107 class vocabulary and 7 class affix prefixes for complete gesture recognition. Shirt color, room brightness, and webcam quality affect detection rates. Optimal detection accuracy is 87.74% and subpar 58.02%. Despite these limitations, the strategy helps deaf students communicate more effectively with non-sign language speakers. This program improves inclusivity and communication in schools, making learning easier for hearing-impaired pupils. This work provides a reliable and quick sign language identification system to help deaf educators and caregivers with daily interactions and education.

Csb-yolo: a rapid and efficient real-time algorithm for classroom student behavior detection

Csb-yolo: a rapid and efficient real-time algorithm for classroom student behavior detection

Real-time detection and geometric analysis algorithm for concrete cracks based on the improved U-net model

Real-time detection and geometric analysis algorithm for concrete cracks based on the improved U-net model

SE-CBAM-YOLOv7: An Improved Lightweight Attention Mechanism-Based YOLOv7 for Real-Time Detection of Small Aircraft Targets in Microsatellite Remote Sensing Imaging

Addressing real-time aircraft target detection in microsatellite-based visible light remote sensing video imaging requires considering the limitations of imaging payload resolution, complex ground backgrounds, and the relative positional changes between the platform and aircraft. These factors lead to multi-scale variations in aircraft targets, making high-precision real-time detection of small targets in complex backgrounds a significant challenge for detection algorithms. Hence, this paper introduces a real-time aircraft target detection algorithm for remote sensing imaging using an improved lightweight attention mechanism that relies on the You Only Look Once version 7 (YOLOv7) framework (SE-CBAM-YOLOv7). The proposed algorithm replaces the standard convolution (Conv) with a lightweight convolutional squeeze-and-excitation convolution (SEConv) to reduce the computational parameters and accelerate the detection process of small aircraft targets, thus enhancing real-time onboard processing capabilities. In addition, the SEConv-based spatial pyramid pooling and connected spatial pyramid convolution (SPPCSPC) module extracts image features. It improves detection accuracy while the feature fusion section integrates the convolutional block attention module (CBAM) hybrid attention network, forming the convolutional block attention module Concat (CBAMCAT) module. Furthermore, it optimizes small aircraft target features in channel and spatial dimensions, improving the model’s feature fusion capabilities. Experiments on public remote sensing datasets reveal that the proposed SE-CBAM-YOLOv7 improves detection accuracy by 0.5% and the mAP value by 1.7% compared to YOLOv7, significantly enhancing the detection capability for small-sized aircraft targets in satellite remote sensing imaging.

Improved Particle Filter Algorithm for Multi-Target Detection and Tracking.

In modern radar detection systems, the particle filter technique has become one of the core algorithms for real-time target detection and tracking due to its good nonlinear and non-Gaussian system state estimation capability. However, when dealing with complex dynamic scenes, the traditional particle filter algorithm exposes obvious deficiencies. The main expression is that the sample degradation is serious, which leads to a decrease in estimation accuracy. In multi-target states, the algorithm is difficult to effectively distinguish and stably track each target, which increases the difficulty of state estimation. These problems limit the application potential of particle filter technology in multi-target complex environments, and there is an urgent need to develop a more advanced algorithmic framework to enhance its robustness and accuracy in complex scenes. Therefore, this paper proposes an improved particle filter algorithm for multi-target detection and tracking. Firstly, the particles are divided into tracking particles and searching particles. The tracking particles are used to maintain and update the trajectory information of the target, and the searching particles are used to identify and screen out multiple potential targets in the environment, to sufficiently improve the diversity of the particles. Secondly, the density-based spatial clustering of applications with noise is integrated into the resampling phase to improve the efficiency and accuracy of particle replication, so that the algorithm can effectively track multiple targets. Experimental result shows that the proposed algorithm can effectively improve the detection probability, and it has a lower root mean square error (RMSE) and a stronger adaptability to multi-target situation.

Real Time Anomaly Detection in Network Traffic: A Comparative Analysis of Machine Learning Algorithms

In the constantly changing field of cybersecurity, real-time intrusion detection using machine learning algorithms has become crucial for protecting network infrastructures. This paper presents a comprehensive literature survey focusing on the comparative study of diverse machine learning algorithms employed for anomaly detection in network traffic. The objective is to critically evaluate the effectiveness of various algorithms in identifying and mitigating threats in real-time scenarios. The study delves into the nuances of prominent machine learning models, including Decision Trees, Random Forests, Support Vector Machines, Neural Networks, and ensemble methods, as they apply to the domain of anomaly detection. Each algorithm is scrutinized based on its ability to adapt to dynamic network behaviors, handle imbalanced datasets, and provide accurate real-time threat assessments. Throughout the survey, key research contributions are analyzed, encompassing methodologies, datasets, and performance metrics. Comparative insights are provided to emphasize the strengths and weaknesses of each algorithm, elucidating their appropriateness for real-time intrusion detection in network traffic. Notably, the examination extends beyond traditional approaches, exploring recent advancements such as deep learning and ensemble techniques. The findings from this comparative study aim to provide practitioners and researchers with valuable insights into selecting the most suitable machine learning algorithm for real-time anomaly detection in the context of network security. By understanding the comparative performance of these algorithms, organizations can make informed decisions to enhance their cybersecurity posture and fortify their defenses against emerging threats.

UNDERSTANDING YOLO: REAL-TIME OBJECT DETECTION EXPLAINED

YOLO (You Only Look Once) is a groundbreaking real-time object detection algorithm known for its speed and accuracy. This article provides an in-depth exploration of YOLO, from its innovative architecture to its practical applications. We discuss its grid-based approach, prediction of bounding boxes and class probabilities, and the use of Non-Max Suppression to refine detections. Additionally, we cover the training process, advantages, and future developments of YOLO, highlighting its significant impact on fields such as autonomous driving, surveillance, and healthcare. Keywords: YOLO, real-time object detection, algorithm, bounding boxes, Non-Max Suppression.

Deteksi Jalan Berlubang Menggunakan Algoritma Yolov5

The city of Sorong, as one of the largest cities in the Southwest Papua region, is facing serious problems due to potholes in various areas. This problem causes the risk of accidents and vehicle damage, disrupts the mobility of city residents, and hinders sustainable infrastructure development. Therefore, we need a system that is efficient and accurate in detecting potholes quickly. In this study, researchers used the "You Only Look Once" (YOLO v5) method to detect potholes in Sorong City. YOLO v5 is a real-time object detection algorithm that has been proven to have high speed and accuracy in recognizing objects. This approach allows for instant detection of potholes as vehicles pass by, so authorities can quickly take the necessary remedial action. This study aims to implement pothole detection technology using YOLO v5, with the hope of increasing efficiency and accuracy in overcoming potholes in Sorong City and improving traffic safety by detecting potholes in real-time. The results of this study obtained the highest training precision, namely 96.6% and the accuracy value of the pothole detection system test.

Federated learning secure model: A framework for malicious clients detection

The Federated Learning Secure Model Repository presents a novel paradigm to ensure the trustworthiness of machine learning models generated through federated learning. It employs advanced algorithms for real-time detection of malicious contributions, supplemented by varied model auditing methods. By leveraging continuous monitoring, the repository guarantees the integrity and security of shared models, thereby mitigating the risks posed by malicious actors. This innovative approach fortifies the collaborative nature of federated learning while safeguarding against potential model poisoning and maintaining the reliability of the shared model ecosystem.

CAC-YOLOv8: real-time bearing defect detection based on channel attenuation and expanded receptive field strategy

Bearing defect detection plays a crucial role in the intelligent production of chemical transmission equipment, where timely identification and handling of defective bearings are essential. However, in practical large-scale industrial production, product surface defects are often complex, diverse, and exhibit significant variations in appearance, posing severe challenges to the discriminative ability and detection efficiency of bearing defect detection algorithms. This paper proposes a real-time bearing surface defect detection algorithm, CAC-YOLOv8, which designs the Channel Attenuation Network (CAN) and Compound Pooling Pyramid Spatial Pyramid Pooling Fast (CPPSPPF) structure. Specifically, the model introduces the Channel Attenuation Network to achieve parallel feature extraction, deep feature processing, and feature fusion under different channel numbers, capturing critical features related to bearing defects and thereby improving the inference speed. Subsequently, based on the concept of overlapped receptive fields, a CPPSPPF structure is constructed, utilizing multiple iterations of max-pooling operations with smaller pooling kernel sizes to prevent information loss while expanding the receptive field, thereby strengthening the capturing ability of features at different scales. The experimental results indicate that the proposed CAC-YOLOv8 bearing surface defect detection algorithm, compared to the YOLOv8 model, achieved a 0.3% improvement in mAP@0.5, reduced model size by 14.4%, and enhanced model inference speed by 33.3%. This enables the CAC-YOLOv8 model to significantly improve the real-time performance of bearing defect detection while maintaining high-precision detection. The performance in practical industrial detection demonstrates that the proposed approach has achieved outstanding results in both speed and accuracy.

Efficient Real time Zynq 7000 FPGA deployment of optimized YOLOv2 deep leaning model for target detection, based on HDL Coder Methodology

Field Programmable Gate Arrays (FPGAs) have garnered significant attention in the development and enhancement of target identification algorithms that employ YOLOv2 models and FPGAs, owing to their adaptability and user-friendliness. The Simulink HDL compiler was utilized to design, simulate, and implement our proposed design. In an effort to rectify this, this paper presents a comprehensive programming and design proposal. The implementation of the YOLOv2 algorithm for real-time vehicle detection on the Xilinx® Zynq-7000 System-on-a-chip is proposed in this work. Real-time testing of the synthesised hardware revealed that it can process Full HD video at a rate of 16 frames per second. On the Xilinx Zynq-7000 SOC, the estimated dynamic power consumption is less than 90 mW. When comparing the results of the proposed work to those of other simulations, it is observed that resource utilization is enhanced by around 204 k (75%) LUT, 305 (12%) DSP, and 224 k (41%) flip-flops at 200 MHz.