Aug 2026· Journal of Visualized Experiments· Vol 234· 0 citations
Medicine
TL;DR
A production-line safety monitoring system that integrates You Only Look Once version 11 (YOLOv11) with a convolutional neural network (CNN) that demonstrated robust performance under complex conditions such as shading and varying lighting, supporting its effectiveness and practical application potential in production line safety monitoring.
Abstract
With the deepening of Industry 4.0, the automation and intelligence levels of production lines have significantly improved, placing higher demands on the real-time performance, accuracy, and safety of monitoring. Traditional monitoring systems, relying on manual inspections or simple threshold-based decisions, generally suffer from slow response times, high false alarm rates, and limited intelligence. Therefore, this paper proposes a production-line safety monitoring system that integrates You Only Look Once version 11 (YOLOv11) with a convolutional neural network (CNN). First, the acquired images were preprocessed. Then, YOLOv11 was used to identify workers, equipment, and potential hazards in real time. Next, an enhanced CNN network with multi-scale feature fusion and attention mechanisms was introduced to improve the feature extraction capabilities for small and occluded targets. Finally, the detection results were fused with the CNN-enhanced features to assess safety status. Experiments were conducted using a self-built production-line safety dataset, employing the stochastic gradient descent (SGD) optimizer with momentum 0.9, an initial learning rate of 0.01, weight decay of 0.0005, cosine-annealed learning rate adjustment, a batch size of 32, and training for 200 epochs. mAP@0.5 and detection speed in frames per second (FPS) were used as evaluation metrics for comparison with the evaluated baseline algorithms. The results show that the proposed system achieved an mAP@0.5 of 0.91 and a detection speed of 120 FPS. It also demonstrated robust performance under complex conditions such as shading and varying lighting, supporting its effectiveness and practical application potential in production line safety monitoring.
The high rate of growth in road traffic has been a major cause of drastic increase in the number of accidents which have resulted in high demand of effective real-time monitoring and response systems. This paper introduces a hybrid system of accident detection and classification of severity based on CCTV video streams implemented on deep learning. The suggested system is a combination of the YOLOv8s object detector model and a Convolutional Neural Network (CNN) using MobileNetV3 to guarantee the efficient performance of the system. YOLOv8s is used to identify regions of accidents in the continuous video frames and the CNN model is used to categorize the identified accidents into moderate and severe. The system is trained on a publicly available dataset of more than 15,000 annotated images, which were recorded in a variety of environmental conditions, which guarantees a robust system and generalization. Experimental findings indicate that it has a high detection accuracy with good precision, recall and mAP values and stable classification accuracy. Also, an automated alert system is included to inform emergency services in case of severe accidents identified so that they can respond quicker and have a lesser effect. The suggested framework is minimal and scalable and appropriate to be implemented in intelligent transportation systems and smart city applications.
J. Viswanath, Jyothinagaram Harika, Gajulapalli Karishma et al.· 2026 4th International Confe...· 0 citations
The mixing stage of automotive battery production requires reliable monitoring of raw material types, personnel actions, and correct tool use. However, accurate multi-scale object detection in complex scenes remains challenging because of background interference and the requirements of embedded deployment and real-time operation. This study proposes an enhanced YOLOv5 framework for industrial multi-object detection. The method adopts a dual-stage feature-enhancement strategy designed to improve robustness while limiting parameter count and computational complexity. First, the Convolutional Block Attention Module (CBAM) is embedded in the Backbone and Neck of YOLOv5 to provide multi-granularity feature enhancement and improve the detection of small objects, such as tools. Second, the conventional CIOU loss is replaced with the Focal-EIOU loss function to optimize bounding-box regression, reduce false detections across multiple target scales, and accelerate model convergence. Finally, K-means clustering is applied to target geometric features to generate specialized anchor-box parameters better suited to industrial scenarios. Experimental results on an automotive battery production-site dataset show that the improved model's mAP@0.5 increased by 2.4 % compared with the original model, reaching 95.2 %, while mAP@0.5:0.95 improved by 3.6 %, reaching 78.5 %. The proposed framework provides a lightweight and reliable solution for multi-object detection in complex industrial environments and supports the development of intelligent visual monitoring systems for smart manufacturing.
Y.-X. Li, H. Chen, L. Zhou· Advances in Production Engin...· 0 citations
The increasing need for automated quality control techniques has been driven by the rapid advancement of smart manufacturing systems within the Industry 4.0 framework. Machine vision using deep learning technologies has emerged as a key enabler of streamlined inspection and decision-making workflows across various production environments. This systematic review analyzes the evolution of deep learning models from traditional Convolutional Neural Networks (CNNs) to transformer-based architectures, examining how these developments address challenges in defect detection and classification on modern industrial production lines. Several key findings emerge from our analysis. First, while CNNs continue to dominate due to well-established performance in local feature extraction, transformer models demonstrate superior accuracy for complex defect geometries through global contextual reasoning, albeit at higher computational costs. Second, real-time detection methods such as YOLO have proven viable for high-velocity production environments, though they typically sacrifice accuracy for speed. Third, integration with edge computing, digital twins, and IoT infrastructure is essential for developing scalable quality control systems. Additionally, ongoing challenges include data format inconsistency, model interpretability, domain transfer limitations, and the gap between controlled development conditions and actual factory environments. This review concludes by outlining future research priorities: developing hybrid CNN-Transformer models, establishing standard evaluation criteria, creating resource-efficient edge applications, and building collaborative human-AI frameworks, thereby providing guidance for researchers and engineers advancing automated visual inspection systems.
Z. Mighouar, J. Melloui, Khalifa Mansouri et al.· Discover Mechanical Engineer...· 0 citations
Tobacco production improves industrial safety through computer vision and deep learning. Tobacco processing plants use end-to-end machine vision systems to detect hazardous conditions in real-time. A comprehensive study on operational risk situations has begun, constructing an annotated dataset that includes various hazardous cases occurring in different enterprises or factories under different lighting conditions. This paper provides an improved optimized backbone structure for the backpropagation convolutional neural network. Add environment-dependent and adaptive preprocessing activation functions to improve detection accuracy. Experiments have shown that it can meet strict standards under varying degrees of severe hazards, proving its effectiveness. The results indicate that compared to known benchmarks, it has higher detection accuracy, recall rate, and robustness in various high-complexity or low-visibility environments. Practical applications have shown that the system has an effective response capability to threats, capable of reducing false positives and intervening quickly. Based on the above research results, it can be predicted that improvements in machine vision-based intelligent systems will enhance industrial safety and be widely used across various industries. Develop fundamental methods based on computer vision technology to automate and optimize safety management systems in the ever-changing industrial environment.
Xiumei Wen· International Conference on...· 0 citations
Over the past few years, object detection has experienced remarkable progress and development, primarily driven by the development of one-stage and two-stage detection algorithms. Among these, Faster region-based convolutional neural network (Faster R-CNN) and you only look once (YOLO) have achieved notable success due to their strong performance and computational efficiency. Object detection plays a crucial role in various applications, particularly in autonomous driving systems, where accurate detection of pedestrians, vehicles, and road signs is essential for ensuring safety and reliability. This paper conducts a comparative evaluation of YOLO and Faster R-CNN to analyze their performance in autonomous driving environments. The experiments were conducted using the KITTI open-source dataset, which is widely used for benchmarking object detection models. All experiments were performed on an NVIDIA RTX A5000 GPU to ensure efficient computation, with implementations developed using Python version 3.9.13. The experimental findings indicate that YOLO surpasses Faster R-CNN in performance, attaining an accuracy rate of 90%. These findings highlight the effectiveness of YOLO for real-time object detection tasks, making it a suitable and preferred choice for time-sensitive applications such as autonomous driving systems.
Madhura M. Bhosale, Y. Angal· IAES International Journal o...· 0 citations
The recent progress in imaging sensors, artificial intelligence, real-time processing hardware has led to a change in industrial quality inspection, where machine vision systems have become a key idea. The conventional manual inspection processes can be quite tedious, irregular and inapplicable to high production throughput. Machine vision systems offer fully automated, objective and repeatable inspection systems with benefits that improve the quality of the products, minimizes the cost of operations and acquires the industry with high standards. The paper is a thorough examination of machine vision systems used in industrial quality inspection especially in the areas of system architecture, image acquisition, preprocessing, feature extraction, defect detection and decision making processes. The incorporation of classical computer vision methodology with the latest deep learning systems like convolutional neural networks has achieved a high rate of quality defect detection, high resilience, and scalability in various manufacturing industries such as the automobile, electronics, pharmaceutical, and food processing industries. Moreover, the paper discusses the issues of inconsistency in lighting, real-time, imbalance in the data set, and system integration with industrial automation systems. Recent studies experimental tests have indicated that machine vision based inspection systems have the capability of having accuracy above 98% which is far much higher when compared to traditional rule based method. At the end of the paper, some future trends have been mentioned like edge-based vision system, explainable artificial intelligence, and Industry 4.0 integration which will likely characterize the future of intelligent inspection systems.
M. Bianchi· International Journal of Mod...· 0 citations
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