Search Results (569)

With the development of precision livestock farming, in order to achieve the goal of fine management and improve the health and welfare of dairy cows, research on dairy cow motion monitoring has become particularly important. In this study, considering the problems surrounding a large amount of model parameters, the poor accuracy of multi-target tracking, and the nonlinear motion of dairy cows in dairy farming scenes, a lightweight detection model based on improved YOLO v11n was proposed and four tracking algorithms were compared. Firstly, the Ghost module was used to replace the standard convolutions in the YOLO v11n network and a more lightweight attention mechanism called ELA was replaced, which reduced the number of model parameters by 18.59%. Then, a loss function called SDIoU was used to solve the influence of different cow target sizes. With the above improvements, the improved model achieved an increase of 2.0 percentage points and 2.3 percentage points in mAP@75 and mAP@50-95, respectively. Secondly, the performance of four tracking algorithms, including ByteTrack, BoT-SORT, OC-SORT, and BoostTrack, was systematically compared. The results show that 97.02% MOTA and 89.81% HOTA could be achieved when combined with the OC-SORT tracking algorithm. Considering the demand of equipment in lightweight models, the improved object detection model in this paper reduces the number of model parameters while offering better performance. The OC-SORT tracking algorithm enables the tracking and localization of cows through video surveillance alone, creating the necessary conditions for the continuous monitoring of cows. Full article

Precise wheat head detection is essential for plant counting and yield estimation in precision agriculture. To tackle the difficulties arising from densely packed wheat heads with diverse scales and intricate occlusions in real-world field conditions, this research introduces YOLO v11n-GRN, an improved wheat head detection model founded on the streamlined YOLO v11n framework. The model optimizes performance through three key innovations: This study introduces a Global Edge Information Transfer (GEIT) module architecture that incorporates a Multi-Scale Edge Information Generator (MSEIG) to enhance the perception of wheat head contours through effective modeling of edge features and deep semantic fusion. Additionally, a C3k2_RFCAConv module is developed to improve spatial awareness and multi-scale feature representation by integrating receptive field augmentation and a coordinate attention mechanism. The utilization of the Normalized Gaussian Wasserstein Distance (NWD) as the localization loss function enhances regression stability for distant small targets. Experiments were, respectively, validated on the self-built multi-temporal wheat field image dataset and the GWHD2021 public dataset. Results showed that, while maintaining a lightweight design (3.6 MB, 10.3 GFLOPs), the YOLOv11n-GRN model achieved a precision, recall, and mAP@0.5 of 92.5%, 91.1%, and 95.7%, respectively, on the self-built dataset, and 91.6%, 89.7%, and 94.4%, respectively, on the GWHD2021 dataset. This fully demonstrates that the improvements can effectively enhance the model’s comprehensive detection performance for wheat ear targets in complex backgrounds. Meanwhile, this study offers an effective technical approach for wheat head detection and yield estimation in challenging field conditions, showcasing promising practical implications. Full article

Vehicle detection plays a pivotal role in traffic management as a key technology for intelligent traffic management and driverless driving. However, current deep learning-based vehicle detection models face several challenges in practical applications. These include slow detection speeds, large computational and parametric quantities, high leakage and misdetection rates in target-intensive environments, and difficulties in deploying them on edge devices with limited computing power and memory. To address these issues, this paper proposes an improved vehicle detection method called SMA-YOLO, based on the YOLOv7 model. Firstly, MobileNetV3 is adopted as the new backbone network to lighten the model. Secondly, the SimAM attention mechanism is incorporated to suppress background interference and enhance small-target detection capability. Additionally, the ACON activation function is substituted for the original SiLU activation function in the YOLOv7 model to improve detection accuracy. Lastly, SIoU is used to replace CIoU to optimize the loss of function and accelerate model convergence. Experiments on the UA-DETRAC dataset demonstrate that the proposed SMA-YOLO model achieves a lightweight effect, significantly reducing model size, computational requirements, and the number of parameters. It not only greatly improves detection speed but also maintains higher detection accuracy. This provides a feasible solution for deploying a vehicle detection model on embedded devices for real-time detection. Full article

Construction sites report the highest rate of industrial accidents, prompting the active development of smart safety management systems based on deep learning-based computer vision technology. To support the digital transformation of construction sites, securing site-specific datasets is essential. In this study, raw data were collected from an actual earthwork site. Key construction equipment and terrain objects primarily operated at the site were identified, and 89,766 images were processed to build a site-specific training dataset. This dataset includes annotated bounding boxes for object detection and polygon masks for instance segmentation. The performance of the dataset was validated using representative models—YOLO v7 for object detection and Mask R-CNN for instance segmentation. Quantitative metrics and visual assessments confirmed the validity and practical applicability of the dataset. The dataset used in this study has been made publicly available for use by researchers in related fields. This dataset is expected to serve as a foundational resource for advancing object detection applications in construction safety. Full article

With the increasing demand for sustainable and cost-effective alternatives in the construction industry, polymer composites have emerged as a promising solution. This study focuses on the development of innovative composite tiles using Linear Low-Density Polyethylene (LLDPE) powder blended with epoxy resin and a hardener as a green substitute for conventional ceramic and cement tiles. LLDPE is recognized for its flexibility, durability, and chemical resistance, making it an effective filler within the epoxy matrix. To optimize its material properties, composite samples were fabricated using three different LLDPE-to-epoxy ratios: 30:70, 40:60, and 50:50. Flexural strength testing revealed that while the 50:50 blend achieved the highest maximum value (29.887 MPa), it also exhibited significant variability, reducing its reliability for practical applications. In contrast, the 40:60 ratio demonstrated more consistent and repeatable flexural strength, ranging from 16 to 20 MPa, which is ideal for flooring applications where mechanical performance under repeated loading is critical. Scanning Electron Microscopy (SEM) images confirmed uniform filler dispersion in the 40:60 mix, further supporting its mechanical consistency. The 30:70 composition showed irregular and erratic behaviour, with values ranging from 11.596 to 25.765 MPa, indicating poor dispersion and increased brittleness. To complement the development of the materials, deep learning techniques were employed for real-time defect detection in the manufactured tiles. Utilizing the YOLOv8 (You Only Look Once version 8) algorithm, this study implemented an automated, vision-based surface monitoring system capable of identifying surface deterioration and defects. A dataset comprising over 100 annotated images was prepared, featuring various surface defects such as cracks, craters, glaze detachment, and tile lacunae, alongside defect-free samples. The integration of machine learning not only enhances quality control in the production process but also offers a scalable solution for defect detection in large-scale manufacturing environments. This research demonstrates a dual approach to material innovation and intelligent defect detection to improve the performance and quality assurance of composite tiles, contributing to sustainable construction practices. Full article

This paper presents a lightweight and cost-effective computer vision solution for automated industrial inspection using You Only Look Once (YOLO) v8 models deployed on embedded systems. The YOLOv8 Nano model, trained for 200 epochs, achieved a precision of 0.932, an mAP@0.5 of 0.938, and an F1-score of 0.914, with an average inference time of ~470 ms on a Raspberry Pi 500, confirming its feasibility for real-time edge applications. The proposed system aims to replace physical jigs used for the dimensional verification of extruded polyamide tubes in the automotive sector. The YOLOv8 Nano and YOLOv8 Small models were trained on a Graphics Processing Unit (GPU) workstation and subsequently tested on a Central Processing Unit (CPU)-only Raspberry Pi 500 to evaluate their performance in constrained environments. The experimental results show that the Small model achieved higher accuracy (a precision of 0.951 and an mAP@0.5 of 0.941) but required a significantly longer inference time (~1315 ms), while the Nano model achieved faster execution (~470 ms) with stable metrics (precision of 0.932 and mAP@0.5 of 0.938), therefore making it more suitable for real-time applications. The system was validated using authentic images in an industrial setting, confirming its feasibility for edge artificial intelligence (AI) scenarios. These findings reinforce the feasibility of embedded AI in smart manufacturing, demonstrating that compact models can deliver reliable performance without requiring high-end computing infrastructure. Full article

With growing global attention on animal welfare and food safety, humane and efficient slaughtering methods in the poultry industry are in increasing demand. Traditional manual inspection methods for stunning broilers need significant expertise. Additionally, most studies on electrical stunning focus on white broilers, whose optimal stunning conditions are not suitable for red-feathered Taiwan chickens. This study aimed to implement a smart, safe, and humane slaughtering system designed to enhance animal welfare and integrate an IoT-enabled vision system into slaughter operations for red-feathered Taiwan chickens. The system enables real-time monitoring and smart management of the poultry stunning process using image technologies for dynamic object tracking recognition. Focusing on red-feathered Taiwan chickens, the system applies dynamic tracking objects with chicken morphology feature extraction based on the YOLO-v4 model to accurately identify stunned and unstunned chickens, ensuring compliance with animal welfare principles and improving the overall efficiency and hygiene of poultry processing. In this study, the dynamic tracking object recognition system comprises object morphology feature detection and motion prediction for red-feathered Taiwan chickens during the slaughtering process. Images are firsthand data from the slaughterhouse. To enhance model performance, image amplification techniques are integrated into the model training process. In parallel, the system architecture integrates IoT-enabled modules to support real-time monitoring, sensor-based classification, and cloud-compatible decisions based on collections of visual data. Prior to image amplification, the YOLO-v4 model achieved an average precision (AP) of 83% for identifying unstunned chickens and 96% for identifying stunned chickens. After image amplification, AP improved significantly to 89% and 99%, respectively. The model achieved and deployed a mean average precision (mAP) of 94% at an IoU threshold of 0.75 and processed images at 39 frames per second, demonstrating its suitability for IoT-enabled real-time dynamic tracking object recognition in a real slaughterhouse environment. Furthermore, the YOLO-v4 model for poultry slaughtering recognition in transient stability, as measured by training loss and validation loss, outperforms the YOLO-X model in this study. Overall, this smart slaughtering system represents a practical and scalable application of AI in the poultry industry. Full article

This paper systematically analyzes security vulnerabilities that may occur during the OpenCV library and IP camera linkage process for the YOLO v10-based IP camera image processing system used in the disaster safety management field. Recently, the use of AI-based real-time image analysis technology in disaster response and safety management systems has been increasing, but it has been confirmed that open source-based object detection frameworks and security vulnerabilities in IP cameras can pose serious threats to the reliability and safety of actual systems. In this study, the structure of an image processing system that applies the latest YOLO v10 algorithm was analyzed, and major security threats (e.g., remote code execution, denial of service, data tampering, authentication bypass, etc.) that might occur during the IP camera image collection and processing process using OpenCV were identified. In particular, the possibility of attacks due to insufficient verification of external inputs (model files, configuration files, image data, etc.), failure to set an initial password, and insufficient encryption of network communication sections were presented with cases. These problems could lead to more serious results in mission-critical environments such as disaster safety management. Full article

The popularity of keeping ornamental fish has grown increasingly, as their vibrant presence can provide a calming influence. Accurately assessing the health of ornamental fish is important but challenging. For this, researchers have focused on developing fish tracking methods that provide trajectories for health assessment. However, issues such as mirror images, occlusion, and motion prediction errors can significantly reduce the accuracy of existing algorithms. To address these problems, we propose a novel ornamental fish tracking method based on deep learning techniques. We first utilize the You Only Look Once (YOLO) v5 deep convolutional neural network algorithm with Distance Intersection over Union–Non Maximum Suppression (DIoU-NMS) to handle occlusion problems. We then design an object removal algorithm to eliminate fish mirror image coordinates. Finally, we adopt an improved DeepSORT algorithm, replacing the original Kalman Filter with an advanced Noise Scale Adaptive (NSA) Kalman Filter to enhance tracking accuracy. In our experiment, we evaluated our method in three simulated real-world fish tank environments, comparing it with the YOLOv5 and YOLOv7 methods. The results show that our method can increase Multiple Object Tracking Accuracy (MOTA) by up to 13.3%, Higher Order Tracking Accuracy (HOTA) by up to 10.0%, and Identification F1 Score by up to 14.5%. These findings confirm that our object removal algorithm effectively improves Multiple Object Tracking Accuracy, which facilitates early disease detection, reduces mortality, and mitigates economic losses—an important consideration given many owners’ limited ability to recognize common diseases. Full article

Fire detection primarily relies on sensors such as smoke detectors, heat detectors, and flame detectors. However, due to cost constraints, it is impractical to deploy such a large number of sensors for fire detection in outdoor gardens and landscapes. To address this challenge and aiming to enhance fire detection accuracy in gardens while achieving lightweight design, this paper proposes an improved symmetry SSS-YOLOv8 model for lightweight fire detection in garden video surveillance. Firstly, the SPDConv layer from ShuffleNetV2 is used to preserve flame or smoke information, combined with the Conv_Maxpool layer to reduce computational complexity. Subsequently, the SE module is introduced into the backbone feature extraction network to enhance features specific to fire and smoke. ShuffleNetV2 and the SE module are configured into a symmetric local network structure to enhance the extraction of flame or smoke features. Finally, WIoU is introduced as the bounding box regression loss function to further ensure the detection performance of the symmetry SSS-YOLOv8 model. Experimental results demonstrate that the improved symmetry SSS-YOLOv8 model achieves precision and recall rates for garden flame and smoke detection both exceeding 0.70. Compared to the YOLOv8n model, it exhibits a 2.1 percentage point increase in mAP, while its parameter is only 1.99 M, reduced to 65.7% of the original model. The proposed model demonstrates superior detection accuracy for garden fires compared to other YOLO series models of the same type, as well as different types of SSD and Faster R-CNN models. Full article

Aiming at the challenges in practical production lines, including the difficulty in accurately capturing external defects on continuously rolling walnuts, distinguishing subtle defects, and differentiating narrow fissures from natural walnut textures, this paper proposes an improved walnut external defect detection model named YOLO11-GME, based on YOLO11n. Firstly, the original backbone network is replaced with the lightweight GhostNetV1 network, enhancing model precision while meeting real-time detection speed requirements. Secondly, a Mixed Local Channel Attention (MLCA) mechanism is incorporated into the neck to strengthen the network’s ability to capture features of subtle defects, thereby improving defect recognition accuracy. Finally, the EIoU loss function is adopted to enhance the model’s localization capability for irregularly shaped defects and reduce false detection rates by improving the scale sensitivity of bounding box regression. Experimental results demonstrate that the improved YOLO11-GME model achieves a mean Average Precision (mAP) of 96.2%, representing improvements of 8.6%, 7%, and 5.8% compared to YOLOv5n, YOLOv8n, and YOLOv10n, respectively, and a 5.9% improvement over the original YOLOv11. Precision rates for the normal, fissure, and inferior categories increased by 8.7%, 5.3%, and 3.7%, respectively. The frame rate remains at 43.92 FPS, approaching the original model’s 51.02 FPS. These results validate that the YOLO11-GME model enhances walnut external defect detection accuracy while maintaining real-time detection speed, providing robust technical support for defect detection and classification in industrial walnut production. Full article

Accurately identifying driver fatigue in complex driving environments plays a crucial role in road traffic safety. To address the challenge of reduced fatigue detection accuracy in complex cabin environments caused by lighting variations, we propose YOLO-FDCL, a novel algorithm specifically designed for driver fatigue detection under complex lighting conditions. This algorithm introduces MobileNetV4 into the backbone network to enhance the model’s ability to extract fatigue-related features in complex driving environments while reducing the model’s parameter size. Additionally, by incorporating the concept of structural re-parameterization, RepFPN is introduced into the neck section of the algorithm to strengthen the network’s multi-scale feature fusion capabilities, further improving the model’s detection performance. Experimental results show that on the YAWDD dataset, compared to the baseline YOLOv8-S, precision increased from 97.4% to 98.8%, recall improved from 96.3% to 97.5%, mAP@0.5 increased from 98.0% to 98.8%, and mAP@0.5:0.95 increased from 92.4% to 94.2%. This algorithm has made significant progress in the task of fatigue detection under complex lighting conditions. At the same time, this model shows outstanding performance on our self-developed Complex Lighting Driving Fatigue Dataset (CLDFD), with precision and recall improving by 2.8% and 2.2%, respectively, and improvements of 3.1% and 3.6% in mAP@0.5 and mAP@0.5:0.95 compared to the baseline model, respectively. Full article

High-precision 6D pose estimation for pick-and-place operations remains a critical problem for industrial robot arms in manufacturing. This study introduces an analytics-based solution for 6D pose estimation designed for a real-world industrial application: it enables the Staubli TX2-60L (manufactured by Stäubli International AG, Horgen, Switzerland) robot arm to pick up metal plates from various locations and place them into a precisely defined slot on a brake pad production line. The system uses a fixed eye-to-hand Intel RealSense D435 RGB-D camera (manufactured by Intel Corporation, Santa Clara, California, USA) to capture color and depth data. A robust software infrastructure developed in LabVIEW (ver.2019) integrated with the NI Vision (ver.2019) library processes the images through a series of steps, including particle filtering, equalization, and pattern matching, to determine the X-Y positions and Z-axis rotation of the object. The Z-position of the object is calculated from the camera’s intensity data, while the remaining X-Y rotation angles are determined using the angle-of-inclination analytics method. It is experimentally verified that the proposed analytical solution outperforms the hybrid-based method (YOLO-v8 combined with PnP/RANSAC algorithms). Experimental results across four distinct picking scenarios demonstrate the proposed solution’s superior accuracy, with position errors under 2 mm, orientation errors below 1°, and a perfect success rate in pick-and-place tasks. Full article

Background/Objectives: The diagnosis of pediatric dental conditions from panoramic radiographs is uniquely challenging due to the dynamic nature of the mixed dentition phase, which can lead to subjective and inconsistent interpretations. This study aims to develop and rigorously validate an advanced deep learning model to enhance diagnostic accuracy and efficiency in pediatric dentistry, providing an objective tool to support clinical decision-making. Methods: An initial comparative study of four state-of-the-art YOLO variants (YOLOv8, v9, v10, and v11) was conducted to identify the optimal architecture for detecting four common findings: Dental Caries, Deciduous Tooth, Root Canal Treatment, and Pulpotomy. A stringent two-tiered validation strategy was employed: a primary public dataset (n = 644 images) was used for training and model selection, while a completely independent external dataset (n = 150 images) was used for final testing. All annotations were validated by a dual-expert team comprising a board-certified pediatric dentist and an experienced oral and maxillofacial radiologist. Results: Based on its leading performance on the internal validation set, YOLOv11x was selected as the optimal model, achieving a mean Average Precision (mAP50) of 0.91. When evaluated on the independent external test set, the model demonstrated robust generalization, achieving an overall F1-Score of 0.81 and a mAP50 of 0.82. It yielded clinically valuable recall rates for therapeutic interventions (Root Canal Treatment: 88%; Pulpotomy: 86%) and other conditions (Deciduous Tooth: 84%; Dental Caries: 79%). Conclusions: Validated through a rigorous dual-dataset and dual-expert process, the YOLOv11x model demonstrates its potential as an accurate and reliable tool for automated detection in pediatric panoramic radiographs. This work suggests that such AI-driven systems can serve as valuable assistive tools for clinicians by supporting diagnostic workflows and contributing to the consistent detection of common dental findings in pediatric patients. Full article

Accurate and timely estimation of soybean emergence at the plot scale using unmanned aerial vehicle (UAV) remote sensing imagery is essential for germplasm evaluation in breeding programs, where breeders prioritize overall plot-scale emergence rates over subimage-based counts. This study proposes PlotCounter, a deep learning regression model based on the TasselNetV2++ architecture, designed for plot-scale soybean seedling counting. It employs a patch-based training strategy combined with full-plot validation to achieve reliable performance with limited breeding plot data. To incorporate additional agronomic information, PlotCounter is extended into a multitask learning framework (MTL-PlotCounter) that integrates sowing metadata such as variety, number of seeds per hole, and sowing density as auxiliary classification tasks. RGB images of 54 breeding plots were captured in 2023 using a DJI Mavic 2 Pro UAV and processed into an orthomosaic for model development and evaluation, showing effective performance. PlotCounter achieves a root mean square error (RMSE) of 6.98 and a relative RMSE (rRMSE) of 6.93%. The variety-integrated MTL-PlotCounter, V-MTL-PlotCounter, performs the best, with relative reductions of 8.74% in RMSE and 3.03% in rRMSE compared to PlotCounter, and outperforms representative YOLO-based models. Additionally, both PlotCounter and V-MTL-PlotCounter are deployed on a web-based platform, enabling users to upload images via an interactive interface, automatically count seedlings, and analyze plot-scale emergence, powered by a multimodal large language model. This study highlights the potential of integrating UAV remote sensing, agronomic metadata, specialized deep learning models, and multimodal large language models for advanced crop monitoring. Full article