successstory Category: Industrial Safety

The cement industry is the backbone of infrastructure development. But behind the strength of concrete lies a constant battle for worker safety. Cement production facilities are inherently hazardous, with risks like dust inhalation, machinery accidents, and exposure to extreme temperatures. In this environment, Personal Protective Equipment (PPE) becomes a life-saving line of defense.

Here’s the challenge: Maintaining consistent PPE usage by workers remains a major hurdle. Traditional methods like manual inspections are time-consuming, susceptible to inaccuracy, and can disrupt workflow.

This is where Scanflow’s visual inspection solution steps in, leveraging the power of AI for a smarter approach to safety.

How Scanflow Helps Cement Workers Stay Safe

• Real-time monitoring: Scanflow utilizes smart cameras strategically placed throughout the factory. These cameras continuously monitor work zones, ensuring workers are properly equipped with the necessary PPE.
• AI-powered PPE detection: The magic lies in Scanflow’s advanced AI. The system can automatically identify workers and recognize if they’re wearing the correct PPE for the designated area. This includes essentials like hard hats, safety glasses, respirators, and work boots.
• Immediate alerts and prompts: If a worker is missing a crucial piece of equipment, Scanflow triggers real-time alerts. This can be a visual notification displayed on a nearby screen or an audio announcement reminding the worker to put on the missing PPE before proceeding.

Beyond Compliance, Building a Culture of Safety

The benefits of Scanflow extend far beyond ensuring compliance with safety regulations. Here’s how it fosters a proactive safety culture:

• Reduced human error: Manual inspections can be subjective and susceptible to fatigue. Scanflow eliminates human error, providing consistent and accurate monitoring 24/7.
• Empowering workers: Real-time feedback from Scanflow empowers workers to take ownership of their safety. Gentle reminders can prevent accidents before they happen.
• Data-driven insights: Scanflow provides valuable data on PPE usage patterns. This allows safety managers to identify areas where improvement is needed and tailor training programs accordingly.
• Improved incident investigation: In the unfortunate event of an accident, Scanflow’s recorded footage provides valuable evidence for investigation. This can help identify root causes and prevent similar incidents in the future.

Partnership for a Safer Future

The partnership between cement factories and Scanflow represents a significant step forward in industrial safety. By integrating cutting-edge technology with traditional safety practices, these industries can achieve a safer, more efficient working environment. This collaboration highlights the importance of embracing innovation to tackle persistent safety challenges, ensuring that every worker returns home safely at the end of the day.

In conclusion, Scanflow’s visual inspection solution addresses the critical issue of PPE compliance in the cement industry. By providing real-time monitoring, reducing human error, and offering valuable data insights, Scanflow enhances workplace safety, protects workers’ health, and contributes to overall operational efficiency. The future of industrial safety lies in such innovative solutions, making our workplaces safer and more productive.

Introduction – What is a Object Detection Model?

An “object detection model” is an advanced artificial intelligence model tailored for visual tasks such as image recognition, object detection, segmentation, and image generation. Object detection models utilize Vision Transformers and are trained on extensive datasets of images and videos to learn pattern recognition, object classification, and visual content generation. Vision Transformer architectures enable these models to generalize knowledge from vast training data effectively, resulting in superior performance in few-shot and zero-shot inference across various downstream tasks.

The Importance of Custom Object Detection Models in Quality Control

Custom object detection models are deep learning systems that excel in analyzing visual data with exceptional precision. Trained on extensive datasets, these models can identify patterns, detect anomalies, and recognize specific features within images. In QC, custom object detection models offer several key advantages:

• Precision: They deliver higher accuracy than human inspectors by eliminating subjective biases and minimizing errors.
• Speed: These models can analyze images in real-time, accelerating inspection processes.
• Consistency: Custom object detection models provide uniform results, ensuring that quality standards are consistently met.

Visual Inspection and Verification of Switches in Truck Dashboards using a Custom Object Detection Mode

• Model Structure: To tackle this problem, we developed a custom object detection model with 50k adjustable parameters. These parameters enable the model to learn the relationship between switch icons and the target classes in the training data. A model with more parameters can handle greater complexity and process more data. However, the increased number of parameters also makes the model more computationally intensive to train and deploy.




• Training the Custom Object Detection Model:





Training a custom object detection model is more challenging than it seems, requiring substantial GPU power and intensive training with a large dataset. We used approximately 10k real-time images, capturing various switch dashboards from different trucks. This custom model is specifically trained for the inspection of switches and icons using a vast number of images and videos, making it a domain-specific model.

Deployment in EC2

Our custom object detection model, designed for visual inspection and verification tasks, is created using a FastAPI web server and deployed on an Amazon EC2 t2.micro instance. This setup leverages the scalable and reliable infrastructure of EC2 to ensure real-time processing and analysis. The Fast API framework provides a high-performance web interface for interacting with the model, making it accessible and efficient for deployment in various industrial quality control applications.

Real-Time Comparison

When evaluating the performance of our custom object detection model against other object detection models, the results clearly demonstrate the superiority of our model in terms of accuracy and efficiency, particularly in the context of switch and icon verification on truck dashboards.

• Accuracy and Efficiency Our custom object detection model achieves superior accuracy compared to traditional object detection models like SSD, and Faster R-CNN. It excels in real-time applications, ensuring rapid and precise switch and icon verification on truck dashboards without compromising on speed.
• Robustness Across Diverse Condition The custom model is designed to handle the varied and challenging conditions often encountered in real-world scenarios, such as different lighting and occlusions. Its domain-specific training enables it to maintain high performance and reliability, outperforming conventional models in robustness and adaptability.

Performance Comparison between our Custom Model with others to perform this Switch Inspection

We have established an extensive benchmark for this performance test, comparing our model with other leading models. As a result, we can now complete a full truck inspection within 5 minutes, compared to the previous 15 minutes. This improvement allows truck companies to inspect many trucks in significantly less time, without any drop in accuracy or performance.

Conclusion

Custom object detection models represent a significant advancement in quality control for industrial manufacturing. By harnessing the power of AI and machine learning, these models offer unmatched accuracy, speed, and consistency in defect detection and anomaly identification. The applications in verifying switch uniformity in trucks and detecting carbon soot on conveyor belts highlight the transformative potential of these models. As these technologies continue to advance, their integration into QC processes will lead to higher product quality, increased efficiency, and enhanced operational reliability.

The Importance of Safety in the Cement Industry

The cement industry is a vital sector, providing the essential building blocks for infrastructure and construction projects around the world. However, this industry also presents inherent safety challenges. Workers face risks from exposure to dust, hazardous materials, and working at heights. Ensuring worker safety is paramount, not just for the well-being of employees but also for operational efficiency and regulatory compliance.

The Challenge: A Legacy Approach to Safety

Previously, the leading cement manufacturer relied on manual methods to monitor worker safety. Safety personnel conducted routine patrols, manually checking for PPE compliance and adherence to safe work practices. This approach had limitations:

• Limited Coverage: It was impossible to monitor every location in the vast plant simultaneously, leaving areas vulnerable.
• Human Error: Visual inspections are susceptible to fatigue and oversight, potentially missing critical violations.
• Reactive Approach: Incidents might occur before safety personnel could intervene, leading to potential injuries and downtime.

These limitations created a constant concern for worker safety and the potential for costly accidents. The company leadership recognized the need for a more robust and proactive safety solution.

The Solution: Implementing an AI-Powered Safety Net

The company partnered with us to implement a cutting-edge Safety Violation Detection System. This system leverages artificial intelligence (AI) and computer vision technology to achieve real-time, comprehensive safety monitoring. Here’s a deeper dive into the system’s functionalities:

• Smart Cameras with Strategic Placement: Industrial-grade cameras were strategically positioned throughout the plant, covering high-risk areas like clinker kilns, conveyor belts, and loading zones. These cameras provide high-definition video feeds for real-time analysis.
• AI-Powered Object Detection and Recognition: Advanced AI algorithms analyze the video feeds, identifying workers and the specific PPE gear they are wearing. The system can distinguish between different types of PPE, ensuring workers are equipped for the specific task at hand.
• Real-Time Alerts and Notifications: The system generates instant alerts for any detected safety violations. These alerts, categorized by severity, are displayed on a central dashboard accessible to safety managers and supervisors. Additionally, email, text, or WhatsApp notifications can be sent to relevant personnel for immediate intervention.

Scanflow’s Customizable suite of solution

We built Scanflow keeping in mind the diverse needs of various industries. The system can be customized to address specific safety concerns within the plant. In this case, the solution focused on three critical use cases:

• PPE Detection: As mentioned earlier, the system verifies PPE compliance in designated zones. It can even detect if a worker has forgotten a specific item, like safety glasses, prompting them to rectify the situation before continuing their task.
• Hot Material Handling: In areas with extreme temperatures and molten materials, the system ensures workers wear aluminized suits and maintain a safe distance. Heat signatures and proximity to danger zones are analyzed to identify potential hazards.
• Working at Height: Cameras monitor workers at elevated locations, automatically detecting the presence and proper use of fall protection equipment like harnesses and lanyards. This real-time monitoring minimizes the risk of falls from heights, a leading cause of injuries in construction and industrial settings.

The Result: Measurable Improvements and a Culture of Safety

The implementation of the AI-powered Safety Violation Detection System has yielded significant positive outcomes:

• Reduced Safety Incidents: Real-time monitoring and immediate alerts have demonstrably reduced the number of safety incidents within the plant. Early detection and intervention have prevented accidents before they could occur.
• Enhanced Safety Culture: The system fosters a culture of safety awareness among workers. Real-time monitoring serves as a constant reminder of the importance of adhering to safety protocols.
• Data-Driven Decision Making: The system generates valuable data on safety trends and potential hazards. This data allows safety managers to identify areas for improvement, optimize safety protocols, and allocate resources effectively.
• Increased Operational Efficiency: By automating safety monitoring, the system frees up valuable security personnel resources. This allows them to focus on more strategic tasks like safety training and incident investigations.

Looking Ahead: A Model for the Industry

The success story of this leading cement manufacturer serves as a compelling example for the entire industry. By embracing AI-powered safety solutions, companies can create safer work environments, reduce operational costs, and gain valuable insights for continuous improvement. As technology continues to evolve, AI-powered systems will play an increasingly important role in safeguarding workers and ensuring a more secure future for the cement industry.

In the ever-evolving landscape of workplace safety, ensuring compliance with Personal Protective Equipment (PPE) regulations stands as a cornerstone in safeguarding your workforce. We understand the challenges posed by manual monitoring methods and the critical need for a more efficient and reliable solution. That’s why we’re thrilled to introduce our groundbreaking system, poised to revolutionise how you approach PPE compliance monitoring.

At the heart of our innovation lies the fusion of cutting-edge artificial intelligence (AI) technology with the imperative of workplace safety. We’ve developed a sophisticated system that automates the identification of PPE usage among your employees, transcending the limitations of traditional monitoring techniques.

Our system leverages advanced object detection models, including renowned technologies such as the YOLO (You Only Look Once) series and EfficientDet. These models have been meticulously trained on vast datasets, enabling them to accurately recognize and classify PPE items in real-time. From helmets to safety glasses and high-visibility vests, our system ensures unparalleled accuracy in assessing compliance.

The Convolutional Neural Networks model was created by utilizing transfer learning on a YOLO deep learning network base version. The model predicts compliance in four categories: NOT SAFE, SAFE, NoHardHat, NoGloves, NoChinstrap, NoSafetyShoes and NoJacket. It does this by accounting for the presence of safety jackets and hardhats. To train the model, a web-based collection of 7000 photos was gathered from video recordings in realtime CCTV of many building sites. On the test data set, the model yielded an F1 score of 0.97 with an average accuracy and recall rate of 97%. In order to facilitate real-time integration and adoption on building sites, the model incorporates an alarm and a time-stamped report upon detection of a non-”SAFE” category.

Environment Setup

The hardware for the system was utilized to test the real-time processing of the algorithm on the field data. We understand that deploying such technology in real-world scenarios can be daunting, particularly in environments where connectivity is limited or latency issues are prevalent. That’s where our innovative approach comes into play: edge computing. By harnessing the power of Edge GPU hardware, such as the NVIDIA Jetson Nano and Jetson Orin, we’ve brought processing capabilities directly to your plant floor.

The YOLO model is used for transfer learning in the keras framework. The final output layer is modified to output different classes namely — NOTSAFE, SAFE, NoJacket, NoHelmet and other requirements- by changing the filter sizes. The trained weights of the YOLO are used as an initial set of weights for the CNN network and the convolutional and fully connected layers are all opened up for training with the data from different sites. In addition to the above, a code to generate alarms and reports in cases of non-compliance was developed, to increase the utility of the algorithm on different sites.

This means real-time performance without the need for extensive bandwidth or reliance on remote servers. With our system, you can rest assured that PPE compliance monitoring is not only efficient but also respects the privacy of your data.

Data Set Preparation

The important part of training the machine learning algorithm was the collection and preparation of data to aid the validation of the model. The preparation of the dataset is the most time-consuming and critical component as it enabled efficient training and accurate detection by the algorithm. Data is collected by both manual collection and image scraping online. Firstly, for manual collection, data was collected from multiple locations and sites where the videos of ongoing works are recorded. The frames from the videos are later extracted as images. The image capturing was done at an interval of 3 s. The purpose of this data is to have a close approximation of the CCTV video data used by the algorithm to predict non-compliance in real-time.

Secondly, images are scraped from the internet using web-crawlers developed in python using the google_images-download library to gather images. The images were then manually checked for relevance to the study. This filtering involved discarding images with watermarks, synthetically generated images. Data augmentation was performed for 60% of images that were collected through standard augmentations such as flipping, rotating 30 degrees right, and 30 degrees left. The final data set had images and data points for the study. Once the dataset was collected, the data was labeled, a graphical image annotation tool. Annotations were saved as XML files.

We adopted a train-validation-test set with a random split of 90:8:2 for training. We ensured that there was adequate representation of all four classifications in each of the datasets. The generated datasets had annotations in the XML file for each image. The XML files were finally collated into a text file to a code readable format for training and validation purposes.

Results

Upon completion of the three training phases, the network’s ultimate loss was 12.06. The dataset was tested and validated using a confusion matrix, and the accuracy of the model was determined by dividing the total number of predictions by the number of correct predictions. Furthermore, utilizing video footage of individuals wearing PPE, a whole new dataset with a range of settings and backdrops was developed. The algorithm’s performance was then evaluated by testing the trained model on both image and video data.

Where:

Precision is the number of true positive predictions divided by the total number of positive predictions made (true positive + false positive).

Recall is the number of true positive predictions divided by the total number of actual positive instances in the data (true positive + false negative).

Accuracy of the Model

The model has a 96.8% accuracy rate, an F1 score of 0.97, and average precision and recall of 0.97. Based on the test data set, these findings show that the model was predicting with 96.8% accuracy. This indicates that the model was operating consistently and predicting pictures with an overall accuracy of 96.8% in the validation data set, which was likewise attained for the test set. It is shown how the sample output predictions work.

The Dashboard,

A PPE compliance dashboard with charts is a visual tool used to track and monitor adherence to personal protective equipment (PPE) guidelines within a workplace or organization. Here’s a simple description of its key features:

• Interactive Charts: It includes various charts and graphs that visually represent compliance data, such as:
• Bar charts showing compliance rates by department, team, or individual.
• Line graphs illustrating trends in compliance over time.
• Pie charts displaying the distribution of compliance across different types of PPE.

Real-Time Updates: The dashboard offers real-time updates, ensuring that users have access to the most current compliance information.

Drill-Down Capability: Users can drill down into specific data points within the charts to gain deeper insights into compliance patterns and outliers.

And Many more interactive features that can drill down to see the real time frames captured from CCTV cameras.

Our Dashboard has NLI chat feature, integrated with our platform elsAI for an easy communication with the data capture. The user would be able to drive the data by chatting something in one liner.

Example : What is the last 24 hours data? What is most violated PPE? etc..

Conclusion

In conclusion, our system represents a paradigm shift in how you approach PPE compliance monitoring. With unparalleled accuracy, real-time performance, and enhanced data privacy, we’re confident that our solution will redefine workplace safety standards in your plant.

If you’re ready to take the next step towards a safer, more efficient workplace, we invite you to reach out to us for a personalized demonstration of our system.