Real-time Detection Model Research Articles

SP-YOLO: A Real-Time and Efficient Multi-Scale Model for Pest Detection in Sugar Beet Fields.

Beet crops are highly vulnerable to pest infestations throughout their growth cycle, which significantly affects crop development and yield. Timely and accurate pest identification is crucial for implementing effective control measures. Current pest detection tasks face two primary challenges: first, pests frequently blend into their environment due to similar colors, making it difficult to capture distinguishing features in the field; second, pest images exhibit scale variations under different viewing angles, lighting conditions, and distances, which complicates the detection process. This study constructed the BeetPest dataset, a multi-scale pest dataset for beets in complex backgrounds, and proposed the SP-YOLO model, which is an improved real-time detection model based on YOLO11. The model integrates a CNN and transformer (CAT) into the backbone network to capture global features. The lightweight depthwise separable convolution block (DSCB) module is designed to extract multi-scale features and enlarge the receptive field. The neck utilizes the cross-layer path aggregation network (CLPAN) module, further merging low-level and high-level features. SP-YOLO effectively differentiates between the background and target, excelling in handling scale variations in pest images. In comparison with the original YOLO11 model, SP-YOLO shows a 4.9% improvement in mean average precision (mAP@50), a 9.9% increase in precision, and a 1.3% rise in average recall. Furthermore, SP-YOLO achieves a detection speed of 136 frames per second (FPS), meeting real-time pest detection requirements. The model demonstrates remarkable robustness on other pest datasets while maintaining a manageable parameter size and computational complexity suitable for edge devices.

Harnessing advanced hybrid deep learning model for real-time detection and prevention of man-in-the-middle cyber attacks

The growing number of connected devices in smart home environments has amplified security risks, particularly from Man-in-the-Middle (MitM) attacks. These attacks allow cybercriminals to intercept and manipulate communication streams between devices, often remaining undetected. Traditional rule-based methods struggle to cope with the complexity of these attacks, creating a need for more advanced, adaptive intrusion detection systems. This research introduces the AEXB Model, a hybrid deep learning approach that combines the feature extraction capabilities of an AutoEncoder with the classification power of XGBoost. By combining these complementary methods, the model enhances detection accuracy and significantly reduces false positives. The AEXB Model’s methodology encompasses robust preprocessing steps, including data cleaning, scaling, and dimensionality reduction, followed by comprehensive feature engineering and selection techniques, such as Recursive Feature Elimination (RFE) and correlation analysis. By applying this approach to the Intrusion Detection in Smart Home (IDSH) dataset, the model achieves an impressive 97.24% accuracy, demonstrating its effectiveness in identifying anomalous network behavior indicative of MitM attacks. Additionally, the model’s real-time detection capabilities allow for rapid responses to threats, thus providing continuous protection in dynamic smart home environments.

Human-Validated Neural Networks for Precise Amastigote Categorization and Quantification to Accelerate Drug Discovery in Leishmaniasis.

Leishmaniases present a significant global health challenge with limited and often inadequate treatment options available. Traditional microscopic methods for detecting Leishmania amastigotes are time-consuming and error-prone, highlighting the need for automated approaches. This study aimed to implement and validate the YOLOv8 deep learning model for real-time detection, quantification, and categorization of Leishmania amastigotes to enhance drug screening assays. YOLOv8 was trained on 470 images from two microscopes, classifying them into categories such as "infected cells," "intracellular amastigotes," "uninfected cells," and "edge cells." The model's performance was compared to human operators using Pearson and Spearman correlation analyses. YOLOv8 achieved strong performance in detecting "infected cells" (AUC = 0.934) and "intracellular amastigotes" (AUC = 0.846). However, challenges remained in differentiating extracellular amastigotes from background noise (AUC = 0.672). Despite these challenges, the YOLOv8 model effectively minimized human variability in drug screening, providing a reliable and efficient tool for the quantification and categorization of Leishmania amastigotes in drug discovery efforts. While further refinements are required to resolve misclassification issues, the model demonstrates significant potential in enhancing both accuracy and throughput in preclinical assays.

RT-DETRmg: a lightweight real-time detection model for small traffic signs

RT-DETRmg: a lightweight real-time detection model for small traffic signs

YOLOv8s-GSW: a real-time detection model for hexagonal barbed wire breakpoints

YOLOv8s-GSW: a real-time detection model for hexagonal barbed wire breakpoints

Multi-kernel inception aggregation diffusion network for tomato disease detection

Tomato leaf diseases significantly impact the yield and quality of tomatoes during cultivation, the main of which are septoria leaf spot, leaf curl virus, verticillium wilt, and early blight. These diseases necessitate prompt detection and management strategies to mitigate their deleterious effects on crop productivity. Due to the considerable scale variations in diseased tomato leaves, accurate and rapid detection and diagnosis remain challenging. To address the detection of tomato leaf diseases at different scales, we propose a real-time detection model incorporating a Multi-kernel Inception Aggregation Diffusion Network. In this paper, (1) We present a Multi-kernel Inception Aggregation Diffusion Network (MIADN) for the feature processing stage, which facilitates the aggregation and diffusion of multi-scale features across hierarchical levels, benefiting the detection of targets at various scales. (2) We present the Multi-kernel Inception Module (MKIM), designed to extract multi-scale object features using diverse convolution kernels, thereby enhancing the model’s feature fusion and representation capabilities. (3) We incorporate the efficient FasterNet network at the feature extraction stage to preserve feature diversity and improve the model’s ability to extract complex target features. (4) Extensive comparative and ablation experiments demonstrate that our method achieves the mean average precision (mAP50) of 96.6%, surpassing the baseline model by 4.1% and the advanced YOLOv9s model by 2.0%. This method provides an effective solution for high-quality tomato cultivation.

An artificial intelligence-enabled consumables tracking system for medical laboratories

Abstract The medical laboratory plays a crucial role within a hospital setting and is responsible for the examination and analysis of patient specimens to accurately diagnose various ailments. The burden on medical laboratory personnel has significantly increased, particularly in the context of the ongoing global COVID-19 pandemic. Worldwide, the implementation of comprehensive and extended COVID-19 screening programs has placed a significant strain on healthcare professionals. This burden has led to exhaustion among medical employees, limiting their ability to effectively track laboratory resources, such as medical equipment and consumables. Therefore, this study proposed an artificial intelligence (AI)-based solution that contributes to a more efficient and less labor-intensive workflow for medical workers in laboratory settings. With the ultimate goal to reduce the burden on healthcare providers by streamlining the process of monitoring and managing these resources, the objective of this study is to design and develop an AI-based system for consumables tracking in medical laboratories. In this work, the effectiveness of two object detection models, namely, YOLOv5x6 and YOLOv8l, for the administration of consumables in medical laboratories was evaluated and analyzed. A total of 570 photographs were used to create the dataset, capturing the objects in a variety of settings. The findings indicate that both detection models demonstrate a notable capability to achieve a high mean average precision. This underscores the effectiveness of computer vision in the context of consumable goods detection scenarios and provides a reference for the application of real-time detection models in tracking systems within medical laboratories.

Synchronous End-to-End Vehicle Pedestrian Detection Algorithm Based on Improved YOLOv8 in Complex Scenarios.

In modern urban traffic, vehicles and pedestrians are fundamental elements in the study of traffic dynamics. Vehicle and pedestrian detection have significant practical value in fields like autonomous driving, traffic management, and public security. However, traditional detection methods struggle in complex environments due to challenges such as varying scales, target occlusion, and high computational costs, leading to lower detection accuracy and slower performance. To address these challenges, this paper proposes an improved vehicle and pedestrian detection algorithm based on YOLOv8, with the aim of enhancing detection in complex traffic scenes. The motivation behind our design is twofold: first, to address the limitations of traditional methods in handling targets of different scales and severe occlusions, and second, to improve the efficiency and accuracy of real-time detection. The new generation of dense pedestrian detection technology requires higher accuracy, less computing overhead, faster detection speed, and more convenient deployment. Based on the above background, this paper proposes a synchronous end-to-end vehicle pedestrian detection algorithm based on improved YOLOv8, aiming to solve the detection problem in complex scenes. First of all, we have improved YOLOv8 by designing a deformable convolutional improved backbone network and attention mechanism, optimized the network structure, and improved the detection accuracy and speed. Secondly, we introduced an end-to-end target search algorithm to make the algorithm more stable and accurate in vehicle and pedestrian detection. The experimental results show that, using the algorithm designed in this paper, our model achieves an 11.76% increase in precision and a 6.27% boost in mAP. In addition, the model maintains a real-time detection speed of 41.46 FPS, ensuring robust performance even in complex scenarios. These optimizations significantly enhance both the efficiency and robustness of vehicle and pedestrian detection, particularly in crowded urban environments. We further apply our improved YOLOv8 model for real-time detection in intelligent transportation systems and achieve exceptional performance with a mAP of 95.23%, outperforming state-of-the-art models like YOLOv5, YOLOv7, and Faster R-CNN.

Improved deep neural network (EnhanceNet) for real-time detection of some publicly prohibited items

ABSTRACT Public safety is a critical concern, typically addressed through security checks at entrances of public places, involving trained officers or X-ray scanning machines to detect prohibited items. However, many places like hospitals, schools, and event centres lack such resources, risking security breaches. Even with X-ray scanners or manual checks, gaps can be exploited by individuals with malicious intent, posing significant security risks. Additionally, traditional methods, relying on manual inspections and conventional image processing techniques, are often inefficient and prone to high error rates. To mitigate these risks, we propose a real-time detection model – EnhanceNet using a customized Scale-Enhanced Pooling Network (SEP-Net) integrated into the YOLOv4. The innovative SEP-Net enhances feature representation and localization accuracy, significantly contributing to the model’s efficacy in detecting prohibited items. We annotated a custom dataset of nine classes and evaluated our models using different input sizes (608 and 416). The 608 input size achieved a mean Average Precision (mAP) of 74.10% with a detection speed of 22.3 Frames per Second (FPS). The 416 input size showed superior performance, achieving a mAP of 76.75% and a detection speed of 27.1 FPS. These demonstrate that our models are accurate and efficient, making them suitable for real-time applications.

Fully automated unsupervised learning approach for thermal camera calibration and an accurate COVID-19 human temperature tracking

During the past three years, people have suffered greatly from what the World Health Organization called the emerging COVID-19. The world lacked the means and methods for early detection of this virus. Several traditional methods were used for detecting the virus, such as thermometers, remote thermal detection guns, and other conventional methods. Most of these systems monopolized making profits or selling their camera products, with prices for these cameras equipped with temperature detection systems exceeding three thousand dollars. An unsupervised model for real-time detection of thermal face skin temperature is proposed. Despite the scarcity and limited availability of thermal video data, we found and used a database created at Nazarbayev University in Nur-Sultan, Kazakhstan, which contains clips of thermal video and RGB video. The two different videos were calibrated, and the unity was measured by two metrics: SSIM and correlation. Four methods of registration were used to achieve perfect congruence, and congruence was also measured through the two previous metrics. The K-means method was then used to extract clusters, and functions for post-processing were built. The thermal face skin was extracted by multiplying the binary face mask with the thermal face, and the temperature of the face was calculated by taking the average values of the thermal face skin pixels and converting them from Fahrenheit to Celsius. Satisfactory results were obtained, with some temperatures detected within the normal range, others below the normal range, and others higher than this rate.

Real-time detection model of electrical work safety belt based on lightweight improved YOLOv5

Real-time detection model of electrical work safety belt based on lightweight improved YOLOv5

A Lightweight and High-Precision Passion Fruit YOLO Detection Model for Deployment in Embedded Devices.

In order to shorten detection times and improve average precision in embedded devices, a lightweight and high-accuracy model is proposed to detect passion fruit in complex environments (e.g., with backlighting, occlusion, overlap, sun, cloud, or rain). First, replacing the backbone network of YOLOv5 with a lightweight GhostNet model reduces the number of parameters and computational complexity while improving the detection speed. Second, a new feature branch is added to the backbone network and the feature fusion layer in the neck network is reconstructed to effectively combine the lower- and higher-level features, which improves the accuracy of the model while maintaining its lightweight nature. Finally, a knowledge distillation method is used to transfer knowledge from the more capable teacher model to the less capable student model, significantly improving the detection accuracy. The improved model is denoted as G-YOLO-NK. The average accuracy of the G-YOLO-NK network is 96.00%, which is 1.00% higher than that of the original YOLOv5s model. Furthermore, the model size is 7.14 MB, half that of the original model, and its real-time detection frame rate is 11.25 FPS when implemented on the Jetson Nano. The proposed model is found to outperform state-of-the-art models in terms of average precision and detection performance. The present work provides an effective model for real-time detection of passion fruit in complex orchard scenes, offering valuable technical support for the development of orchard picking robots and greatly improving the intelligence level of orchards.

Lightweight cotton diseases real-time detection model for resource-constrained devices in natural environments.

Cotton, a vital textile raw material, is intricately linked to people's livelihoods. Throughout the cotton cultivation process, various diseases threaten cotton crops, significantly impacting both cotton quality and yield. Deep learning has emerged as a crucial tool for detecting these diseases. However, deep learning models with high accuracy often come with redundant parameters, making them challenging to deploy on resource-constrained devices. Existing detection models struggle to strike the right balance between accuracy and speed, limiting their utility in this context. This study introduces the CDDLite-YOLO model, an innovation based on the YOLOv8 model, designed for detecting cotton diseases in natural field conditions. The C2f-Faster module replaces the Bottleneck structure in the C2f module within the backbone network, using partial convolution. The neck network adopts Slim-neck structure by replacing the C2f module with the GSConv and VoVGSCSP modules, based on GSConv. In the head, we introduce the MPDIoU loss function, addressing limitations in existing loss functions. Additionally, we designed the PCDetect detection head, integrating the PCD module and replacing some CBS modules with PCDetect. Our experimental results demonstrate the effectiveness of the CDDLite-YOLO model, achieving a remarkable mean average precision (mAP) of 90.6%. With a mere 1.8M parameters, 3.6G FLOPS, and a rapid detection speed of 222.22 FPS, it outperforms other models, showcasing its superiority. It successfully strikes a harmonious balance between detection speed, accuracy, and model size, positioning it as a promising candidate for deployment on an embedded GPU chip without sacrificing performance. Our model serves as a pivotal technical advancement, facilitating timely cotton disease detection and providing valuable insights for the design of detection models for agricultural inspection robots and other resource-constrained agricultural devices.

YOLOv8n-GAM: an improved surface defect detection network for hot-rolled strip steel

Production defects caused by irresistible factors such as process design problems or differences in steel properties in strip production affect the economic benefits of the enterprise and threaten production safety. Traditional defect detection methods are difficult to achieve real-time and high-precision detection, so developing surface defect detection methods based on deep learning is of great significance for strip production. In order to effectively improve the accuracy of the deep learning model in detecting surface defects on hot-rolled strip, in this work we propose a real-time detection model for surface defects on strip steel based on the YOLOv8n model. Firstly, the newly convolutional layer Con5v is designed to replace the original convolutional layer in the neck, and an attention mechanism is added in front of each Con5v to improve the algorithm’s ability to extract small target information. Secondly, an additional set of upsampled feature extraction units is added to the neck in order to enhance the spatial information of the feature map. Subsequently, a set of feature fusion units is incorporated and the convolutional layers in it are improved to provide better feature maps. Thirdly, the number of decoupling detection heads is increased to receive more high-quality features. The final experimental results show that YOLOv8n-GAM (YOLOv8 Nano Model with Global Attention Mechanism) achieves 81.4mAP and 82.0FPS on the NEU-DET dataset and 71.2mAP and 55.0FPS on the GC10-DET dataset, which are 5.7% and 6.9% higher than those of YOLOv8n, respectively. The model proposed in this paper achieves a comprehensive performance improvement in strip steel.

APHS-YOLO: A Lightweight Model for Real-Time Detection and Classification of Stropharia Rugoso-Annulata.

The classification of Stropharia rugoso-annulata is currently reliant on manual sorting, which may be subject to bias. To improve the sorting efficiency, automated sorting equipment could be used instead. However, sorting naked mushrooms in real time remains a challenging task due to the difficulty of accurately identifying, locating and sorting large quantities of them simultaneously. Models must be deployable on resource-limited devices, making it challenging to achieve both a high accuracy and speed. This paper proposes the APHS-YOLO (YOLOv8n integrated with AKConv, CSPPC and HSFPN modules) model, which is lightweight and efficient, for identifying Stropharia rugoso-annulata of different grades and seasons. This study includes a complete dataset of runners of different grades in spring and autumn. To enhance feature extraction and maintain the recognition accuracy, the new multi-module APHS-YOLO uses HSFPNs (High-Level Screening Feature Pyramid Networks) as a thin-neck structure. It combines an improved lightweight PConv (Partial Convolution)-based convolutional module, CSPPC (Integration of Cross-Stage Partial Networks and Partial Convolution), with the Arbitrary Kernel Convolution (AKConv) module. Additionally, to compensate for the accuracy loss due to lightweighting, APHS-YOLO employs a knowledge refinement technique during training. Compared to the original model, the optimized APHS-YOLO model uses 57.8% less memory and 62.5% fewer computational resources. It has an FPS (frames per second) of over 100 and even achieves 0.1% better accuracy metrics than the original model. These research results provide a valuable reference for the development of automatic sorting equipment for forest farmers.

Establishment and validation of an artificial intelligence-based model for real-time detection and classification of colorectal adenoma

Colorectal cancer (CRC) prevention requires early detection and removal of adenomas. We aimed to develop a computational model for real-time detection and classification of colorectal adenoma. Computationally constrained background based on real-time detection, we propose an improved adaptive lightweight ensemble model for real-time detection and classification of adenomas and other polyps. Firstly, we devised an adaptive lightweight network modification and effective training strategy to diminish the computational requirements for real-time detection. Secondly, by integrating the adaptive lightweight YOLOv4 with the single shot multibox detector network, we established the adaptive small object detection ensemble (ASODE) model, which enhances the precision of detecting target polyps without significantly increasing the model's memory footprint. We conducted simulated training using clinical colonoscopy images and videos to validate the method's performance, extracting features from 1148 polyps and employing a confidence threshold of 0.5 to filter out low-confidence sample predictions. Finally, compared to state-of-the-art models, our ASODE model demonstrated superior performance. In the test set, the sensitivity of images and videos reached 87.96% and 92.31%, respectively. Additionally, the ASODE model achieved an accuracy of 92.70% for adenoma detection with a false positive rate of 8.18%. Training results indicate the effectiveness of our method in classifying small polyps. Our model exhibits remarkable performance in real-time detection of colorectal adenomas, serving as a reliable tool for assisting endoscopists.

Real-time multi-object detection model for cracks and deformations based on deep learning

The paper proposes a deep learning-based multi-object real-time detection model for concrete cracks and structural deformations. The model improves the single-stage object detection framework, You Only Look Once version 7 (YOLOv7), by incorporating convolutional block attention mechanisms and global attention mechanisms into its backbone and neck networks, respectively. It also establishes dual output branches for cracks and deformations within the output module to enable multi-object detection capabilities. Utilizing transfer learning strategies, the model effectively detects concrete cracks and structural deformations with a limited dataset. The results demonstrate that the improved YOLOv7 model significantly improves the detection of non-continuous cracks and reduces noise in complex environments, indicating strong generalization and robustness. The improved model exhibits a 4.53% increase in crack detection accuracy over the original and achieves low peak relative errors for deflection deformation in concrete beams and in-plane deformation in concrete slabs at just 0.22% and 3.05%, respectively.

A Packet Scripting Model for Real-Time Detection of Cyber Attacks

A Packet Scripting Model for Real-Time Detection of Cyber Attacks

Multi-scale feature adaptive fusion model for real-time detection in complex citrus orchard environments

A Multi-scale Feature Adaptive Fusion model (MFAF-YOLO) for real-time detection of citrus harvesting robots in complex field environments is proposed in this study. This proposed model improves detection accuracy while meeting the lightweight requirements of consumer-level cameras. In this study, different citrus fruit varieties, sourced from two distinct devices, are classified into ‘First priority’, ‘Second priority’, and unannotated citrus. This classification strategy guides robots in sequential picking in real-field scenarios, reducing detection redundancy and diminishing the damage rate at the robot end-effector. Additionally, multiple clustering algorithms were employed to adjust the anchor box sizes of the model. The impact of dual and triple detection heads on model accuracy across diverse clustering algorithms was also explored. An innovative multi-scale feature adaptive fusion module was embedded in the model's neck section, aiming to optimize model accuracy and reduce model size. In the dataset processed with multiple augmentation techniques, the novel MFAF-YOLO model achieved the mean Average Precision (mAP) of 90.2 %, reflecting an improvement of 3.8 % compared to the original YOLOv5s model, demonstrating superior generalization capabilities. Compared with seven other mainstream models, including YOLOv4 and MobileNet-YOLOv5s, the Average Precision (AP) value of ‘First priority’ and ‘Second priority’ is 93.2 % and 87.3 %, respectively, achieved the highest AP while maintaining a better detection speed. The model size of MFAF-YOLO is reduced to 10.4 MB, marking a 26.2 % decrease relative to the lightweight YOLOv5s model. Experimental results highlight the model's strong robustness and successful attainment of an effective balance between model accuracy and lightweight. This proposed model provides theoretical support for real-time picking and harvesting decision-making of citrus.

Using an improved lightweight YOLOv8 model for real-time detection of multi-stage apple fruit in complex orchard environments

For the purpose of monitoring apple fruits effectively throughout the entire growth period in smart orchards. A lightweight model named YOLOv8n-ShuffleNetv2-Ghost-SE was proposed. The ShuffleNetv2 basic modules and down-sampling modules were alternately connected, replacing the Backbone of YOLOv8n model. The Ghost modules replaced the Conv modules and the C2fGhost modules replaced the C2f modules in the Neck part of the YOLOv8n. ShuffleNetv2 reduced the memory access cost through channel splitting operations. The Ghost module combined linear and non-linear convolutions to reduce the network computation cost. The Wise-IoU (WIoU) replaced the CIoU for calculating the bounding box regression loss, which dynamically adjusted the anchor box quality threshold and gradient gain allocation strategy, optimizing the size and position of predicted bounding boxes. The Squeeze-and-Excitation (SE) was embedded in the Backbone and Neck part of YOLOv8n to enhance the representation ability of feature maps. The algorithm ensured high precision while having small model size and fast detection speed, which facilitated model migration and deployment. Using 9652 images validated the effectiveness of the model. The YOLOv8n-ShuffleNetv2-Ghost-SE model achieved Precision of 94.1%, Recall of 82.6%, mean Average Precision of 91.4%, model size of 2.6 MB, parameters of 1.18 M, FLOPs of 3.9 G, and detection speed of 39.37 fps. The detection speeds on the Jetson Xavier NX development board were 3.17 fps. Comparisons with advanced models including Faster R-CNN, SSD, YOLOv5s, YOLOv7‑tiny, YOLOv8s, YOLOv8n, MobileNetv3_small-Faster, MobileNetv3_small-Ghost, ShuflleNetv2-Faster, ShuflleNetv2-Ghost, ShuflleNetv2-Ghost-CBAM, ShuflleNetv2-Ghost-ECA, and ShuflleNetv2-Ghost-CA demonstrated that the method achieved smaller model and faster detection speed. The research can provide reference for the development of smart devices in apple orchards.